INTERNATIONAL MONETARY FUND

GLOBAL
FINANCIAL
STABILITY
REPORT

Steadying the Course: Uncertainty,
Artificial Intelligence, and Financial Stability

2024
OCT

INTERNATIONAL MONETARY FUND

GLOBAL
FINANCIAL
STABILITY
REPORT

Steadying the Course: Uncertainty,
Artificial Intelligence, and Financial Stability

2024
OCT

©2024 International Monetary Fund

Cover and Design: IMF CSF Creative Solutions Division
Composition: Absolute Service, Inc.

Cataloging-in-Publication Data

IMF Library

Names: International Monetary Fund.
Title: Global financial stability report.
Other titles: GFSR | World economic and financial surveys, 0258-7440
Description: Washington, DC : International Monetary Fund, 2002- | Semiannual | Some issues also have thematic

titles. | Began with issue for March 2002.

Subjects: LCSH: Capital market—Statistics—Periodicals. | International finance—Forecasting—Periodicals. |

Economic stabilization—Periodicals.

Classification: LCC HG4523.G557

ISBNs:  979-8-40027-757-3 (paper)
979-8-40027-768-9 (ePub)
979-8-40027-761-0 (web PDF)

Disclaimer: The Global Financial Stability Report is a survey by the IMF staff published twice a year,
in the spring and fall. The report draws out the financial ramifications of economic issues highlighted
in  the  IMF’s  World  Economic  Outlook. The  report  was  prepared  by  IMF  staff  and  has  benefited
from  comments  and  suggestions  from  Executive  Directors  following  their  discussion  of  the  report
on October 8, 2024. The views expressed in this publication are those of the IMF staff and do not
necessarily represent the views of the IMF’s Executive Directors or their national authorities.

Recommended citation: International Monetary Fund. 2024. Global Financial Stability Report: Steadying
the Course: Uncertainty, Artificial Intelligence, and Financial Stability. Washington, DC. October.

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CONTENTS

Assumptions and Conventions

Further Information

Preface

Foreword

Executive Summary

IMF Executive Board Discussion of the Outlook, October 2024

Chapter 1. Steadying the Course: Financial Markets Navigate Uncertainty

Chapter 1 at a Glance
Introduction
Monetary and Financial Developments
Growth-at-Risk, the Global Macrofinancial Stability Assessment
Emerging Markets
Corporate Credit
Real Estate
Bank and Nonbank Financial Intermediaries
Policy Recommendations
Box 1.1. Synthetic Risk Transfers: Managing Risks or Creating New Ones?
Box 1.2. Interconnectedness Through Tokenization
Box 1.3. Summertime Blues: The Carry Trade Unwind and VIX Surge of August 2024
Box 1.4. Domestic Investors in Local Bond Markets: A Stabilizing Force?
References

Chapter 2. Macrofinancial Stability Amid High Global Economic Uncertainty

Chapter 2 at a Glance
Introduction
Macroeconomic Uncertainty and Financial Stability: A Conceptual Framework
The Global Landscape of Macroeconomic Uncertainty
Macroeconomic Uncertainty and Downside Risk to Output
Macroeconomic Uncertainty Amplifies the Impact of Macrofinancial Vulnerabilities
The Downside Risks of Macroeconomic Uncertainty and Cross-Border Spillovers
Conclusion and Policy Recommendations
Box 2.1. Market and Credit Channels Are Important in Transmitting the Effect of

Macroeconomic Uncertainty on Output

Box 2.2. Macroeconomic Uncertainty and Policy Credibility
References

Chapter 3. Advances in Artificial Intelligence: Implications for Capital Market Activities

Chapter 3 at a Glance
Introduction
Current and Future Adoption of Artificial Intelligence in Capital Market Activities

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GLOBAL FINANCIAL STABILITY REPORT: STEAdYING ThE COuRSE: uNCERTAINTY, ARTIFICIAL INTELLIGENCE, ANd FINANCIAL STABILITY

The Artificial Intelligence Transformation: Implications for Market Structures

and Dynamics

Financial Stability Implications
Regulatory and Supervisory Developments
Policy Recommendations
Box 3.1. IMF Staff Market Outreach: Qualitative Assessment of Advances in

Artificial Intelligence and Its Implications for Capital Markets

Box 3.2. Manipulation and Cyber Risk: The Artificial Intelligence Arms Race
Glossary
References

Tables

Table 3.1. Potential Impact of the Adoption of Artificial Intelligence in

Algorithmic Trading

Figures

Figure ES.1. Aggregate Debt-to-GDP Ratios
Figure ES.2. Difference of the Standardized Measures of Financial Volatility, Economic

Uncertainty, and Geopolitical Risk

Figure ES.3. Equity Market Volatility in Japan and the United States
Figure ES.4. Effect of Real Economic Uncertainty on Growth-at-Risk by Horizon
Figure ES.5. Spillover to Emerging Market Term Premiums from US Term Premiums
Figure ES.6. Global Issuance of Sustainable Debt
Figure ES.7. Maturing Commercial Real Estate Debt and Funding Gap
Figure ES.8. Banks Signaling Vulnerabilities in Three or More Areas of Risk
Figure ES.9. Patents in Asset Management Related to Artificial Intelligence
Figure 1.1. Market-Implied Expectations of Policy Rates for Selected Advanced and

Emerging Market Economies

Figure 1.2. Economic Uncertainty and Market-Based Inflation Expectations
Figure 1.3. Recent Developments in Longer-Term Interest Rates
Figure 1.4. Drivers of Steepening in the US Yield Curve
Figure 1.5. Shifts in Government Bond Buyer Base Toward Price-Sensitive Investors

amid Progress on Quantitative Tightening

Figure 1.6. Expansion of Primary Dealers’ Treasury Holdings amid Rising Free-Floating

Securities and Share of Bills
Figure 1.7. Global Asset Prices
Figure 1.8. Concentration, Expectations Regarding Growth, and Rotation in the

Current Equity Rally

Figure 1.9. Performance of Assets in Emerging Markets
Figure 1.10. Fading Rally in Crypto Assets
Figure 1.11. Financial Conditions Indices
Figure 1.12. Global Growth-at-Risk
Figure 1.13. Continuing Resilience in Emerging Markets
Figure 1.14. Emerging Market Monetary Policy and Currencies
Figure 1.15. Emerging Market Portfolio Flows
Figure 1.16. Emerging Market Fiscal Buffers and Financial Costs
Figure 1.17. Frontier Market Developments
Figure 1.18. Update on Sustainable Debt Issuance by Emerging Markets
Figure 1.19. Inflation and Asset Developments in China
Figure 1.20. Drill-Down on Chinese Asset Management Companies
Figure 1.21. Corporate Market Dynamics

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International Monetary Fund | October 2024

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Figure 1.22. Corporate Credit Fundamentals
Figure 1.23. Corporate Debt Sustainability
Figure 1.24. Expansion in Private Credit Despite Borrowers’ Struggles
Figure 1.25. Developments in Global Residential Real Estate Markets
Figure 1.26. Developments in Commercial Real Estate Markets
Figure 1.27. Bank Exposures in Commercial Real Estate Offices
Figure 1.28. Broad Resilience in the Banking Sector, with Persistent Weakness Among

Several Small Banks

Figure 1.29. Growth, Outflows, and Leverage of Bond Funds
Figure 1.30. Hedge Funds and Carry Trades
Figure 1.31. Defined-Contribution Pension Funds and Unit-Linked and Annuity Insurers
Figure 1.1.1. Selected Information for SRT Structures, Issuance, and Returns
Figure 1.2.1. Interconnectedness Through Tokenization
Figure 1.3.1. Unwinding of Carry Trades in Early August
Figure 1.4.1. NBFIs Play an Important Role in NBFI Markets
Figure 2.1. Economic and Policy Uncertainty
Figure 2.2. Macroeconomic Uncertainty and Macrofinancial Stability:

Channels of Transmission

Figure 2.3. Measures of Macroeconomic and Financial Uncertainty
Figure 2.4. Uncertainty and Downside Risks to Output: Results from Panel Quantile

GaR Model

Figure 2.5. Improvement in Predictive Accuracy from Applying Machine Learning

GaR Models

Figure 2.6. Macroeconomic Uncertainty and Macrofinancial Vulnerabilities
Figure 2.7. Cross-Border Spillover Effects of Foreign Uncertainty
Figure 2.1.1. Macroeconomic Uncertainty and Tail Risks in Financial Markets
Figure 2.1.2. Macroeconomic Uncertainty and Tail Risks to Bank Loan Growth
Figure 2.2.1. Soundness of Monetary Policy Framework and Effect of Uncertainty on

Future GDP

Figure 3.1. Recent and Potential Use Cases for Artificial Intelligence and

Machine Learning in Capital Market Activities: Investment Decisions, Trade Execution,
and Monitoring Processes

Figure 3.2. The Cost of Compute and the Artificial Intelligence Market Structure
Figure 3.3. Investment Strategies Driven by Artificial Intelligence
Figure 3.4. Opportunities for Artificial Intelligence to Create Value: Asset Classes
Figure 3.5. Artificial Intelligence Advances: Use Cases and Adoption in

Investment Processes

Figure 3.6. Artificial Intelligence/Machine Learning Innovations: Evidence from

Patent Applications

Figure 3.7. Adoption of Artificial Intelligence: Evidence from Candidate Profiles and

Job Vacancies

Figure 3.8. Algorithmic Trading Activity and Concentration in Equity Markets
Figure 3.9. The Risks of Artificial Intelligence: Dependence on Third-Party Providers
Figure 3.10. Algorithmic Trading and Market Efficiency
Figure 3.11. New Artificial Intelligence Trading Dynamics
Figure 3.12. Market Intelligence: Risks and Regulation
Figure 3.13. Standard Setter, National, and Supervisory Artificial Intelligence Initiatives
Figure 3.1.1. Participants in the IMF’s Market Intelligence Outreach
Figure 3.2.1. Cyberattacks and Artificial Intelligence Incidents

CONTENTS

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International Monetary Fund | October 2024

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GLOBAL FINANCIAL STABILITY REPORT: STEAdYING ThE COuRSE: uNCERTAINTY, ARTIFICIAL INTELLIGENCE, ANd FINANCIAL STABILITY

Online-Only Annexes

Online Annex 2.1. Description and Sources
Online Annex 2.2. Measures of Uncertainty
Online Annex 2.3. Does Macroeconomic Uncertainty Help to Predict Downside Risk to Output?
Online Annex 2.4. How Does Macroeconomic Uncertainty Interact With Macrofinancial

Vulnerabilities to Affect Downside Risks to Output?

Online Annex 2.5. Does Macroeconomic Uncertainty Influence Activity Through the

Market Tail Risk and Bank Lending Channels?

Online Annex 2.6. Does the Impact of Macroeconomic Uncertainty Spill Over Across
Borders to Affect Downside Risks to Economic Activity in Major Financial and
Trading Partners?

Online Annex 3.1. Identifying Idiosyncratic Jumps in Stock Returns

Editor’s Note (11/21/24):

The following few changes were made after the initial release of this report.
1. In the Preface, a contributor’s name was transposed.
2. The sources for Figure 3.8 were corrected.
3. The storyboard for Figure 3.11, panel 3, was corrected.

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International Monetary Fund | October 2024

ASSUMPTIONS AND CONVENTIONS

The following conventions are used throughout the Global Financial Stability Report:

. . .  to indicate that data are not available or not applicable;

—  to indicate that the figure is zero or less than half the final digit shown or that the item does not exist;

–

between years or months (for example, 2021–22 or January–June) to indicate the years or months covered,
including the beginning and ending years or months;

/

between years or months (for example, 2021/22) to indicate a fiscal or financial year.

“Billion” means a thousand million.

“Trillion” means a thousand billion.

“Basis points” refers to hundredths of 1 percentage point (for example, 25 basis points are equivalent to ¼ of
1 percentage point).

Minor discrepancies between sums of constituent figures and totals shown reflect rounding.

As used in this report, the terms “country” and “economy” do not in all cases refer to a territorial entity that is a
state as understood by international law and practice. As used here, the term also covers some territorial entities
that are not states but for which statistical data are maintained on a separate and independent basis.

The boundaries, colors, denominations, and any other information shown on the maps do not imply, on the part
of the International Monetary Fund, any judgment on the legal status of any territory or any endorsement or
acceptance of such boundaries.

International Monetary Fund | October 2024

vii

FURTHER INFORMATION

Corrections and Revisions

The data and analysis appearing in the Global Financial Stability Report are compiled by IMF staff at the time
of publication. Every effort is made to ensure their timeliness, accuracy, and completeness. When errors are discov-
ered, corrections and revisions are incorporated into the digital editions available from the IMF website and on the
IMF eLibrary (see below). All substantive changes are listed in the Contents page.

Print and Digital Editions
Print

Print copies of this Global Financial Stability Report can be ordered from the IMF Bookstore at imfbk.st/549858.

Digital

Multiple digital editions of the Global Financial Stability Report, including ePub, enhanced PDF, and HTML,

are available on the IMF eLibrary at eLibrary.IMF.org/GFSROct24.

Download a free PDF of the report and data sets for each of the figures therein from the IMF website at
www.IMF.org/publications/GFSR or scan the QR code below to access the Global Financial Stability Report web
page directly:

Copyright and Reuse

Information on the terms and conditions for reusing the contents of this publication are at

www.IMF.org/external/terms.htm.

viii

International Monetary Fund | October 2024

PREFACE

The Global Financial Stability Report (GFSR) assesses key vulnerabilities the global financial system is exposed
to. In normal times, the report seeks to play a role in preventing crises by highlighting policies that may mitigate
systemic risks, thereby contributing to global financial stability and the sustained economic growth of the IMF’s
member countries.

The analysis in this report was coordinated by the Monetary and Capital Markets (MCM) Department under
the general direction of Tobias Adrian, Director. The project was directed by Mahvash Qureshi, Assistant Director;
Jason Wu, Assistant Director; Charles Cohen, Advisor; Jay Sanat Surti, Division Chief; Nassira Abbas, Deputy
Division Chief and Chapter 3 co-lead; Caio Ferreira, Deputy Division Chief; Sheheryar Malik, Deputy Division
Chief; Mario Catalán, Deputy Division Chief and Chapter 2 co-lead; Dirk Jan Grolleman, Deputy Division Chief
and Chapter 3 co-lead; Benjamin Mosk, Senior Financial Sector Expert and Chapter 3 co-lead; and Andrea Deghi,
Financial Sector Expert and Chapter 2 co-lead. It benefited from comments and suggestions from the senior staff
in the MCM Department.

Individual contributors to the report were Rafael Barbosa, Yuhua Cai, Mustafa Oğuz Çaylan, Yingyuan
Chen, Fabio Cortes, Mohamed Diaby, Gonzalo Fernandez Dionis, Andrew Ferrante, Deepali Gautam, Sanjay
Hazarika, Esti Kemp, Johannes S. Kramer, Harrison Samuel Kraus, Jiayi Li, Yiran Li, Xiang-Li Lim, Li Lin,
Corrado Macchiarelli, Saurav Mazumdar, Kleopatra Nikolaou, Tatsushi Okuda, Sonal Patel, Silvia Loyda Ramirez,
Francesco de Rossi, Patrick Schneider, Puja Singh, Richard Stobo, Gabriela Elizabeth Conde Vitureira, Jeffrey
David Williams, Tao Wu, Dmitry Yakovlev, Mustafa Yasin Yenice, Aki Yokoyama, and Aleksandr Zotov. Stefano
Giglio of Yale University and Ian Dew-Becker of the Federal Reserve Bank of Chicago served as expert advisors for
Chapter 2. Markus Pelger of Stanford University served as an expert advisor for Chapter 3. Monica Devi, Javier
Chang, Lauren Kao, and Srujana Sammeta were responsible for excellent coordination and editorial support.
Rumit Pancholi from the Communications Department led the editorial team and managed the report’s

production, with editorial and typesetting service from Grauel Group and Absolute Service, Inc.

This issue of the GFSR draws in part on a series of discussions with banks, securities firms, asset management
companies, hedge funds, standard setters, financial consultants, pension funds, trade associations, central banks,
national treasuries, and academic researchers.

This GFSR reflects information available as of September 30, 2024. The report benefited from comments and
suggestions from staff in other IMF departments, as well as from Executive Directors following their discussions of
the GFSR on October 8, 2024. However, the analysis and policy considerations are those of the contributing staff
and should not be attributed to the IMF, its Executive Directors, or their national authorities.

International Monetary Fund | October 2024

ix

FOREWORD

T

he Global Financial Stability Report (GFSR)
has repeatedly highlighted how the financial
regulatory reforms implemented since the
global financial crisis have helped support

global financial stability. Strengthened supervision and
regulation, better crisis management preparedness and
resolution processes, enhanced data collection, and a
macroprudential approach to financial sector oversight
have raised the financial sector’s resilience to the multi-
tude of shocks experienced in recent years.

Good policymaking, however, is a forward-looking

activity. This is especially relevant today, as the
structure of the global financial system is undergoing
substantial transformations, most notably with the
growing participation of nonbanks in financial inter-
mediation (NBFI). While these changes have been in
train for some time, the magnitudes are becoming ever
larger, with several drivers: (i) constraints imposed by
postcrisis regulations on banks’ leverage have encour-
aged diversification and the transfer of risk to other
financial intermediaries; (ii) pension funds, insurance
companies, and sovereign wealth funds have grown
from 50 percent of global GDP to close to 90 percent
over the past two decades, with much of their assets
deployed to nonbanks; and (iii) new digital technol-
ogies are revolutionizing how financial services are
distributed, how credit is evaluated, and how trading
and market making are conducted.

This evolving nature of the financial sector land-
scape has positive implications for financial markets
and economies worldwide. Market-based finance and
nonbank credit intermediation have generated alter-
native sources of financing for firms, better capital allo-
cation, and greater market efficiency through capital
markets activity, private equity and private credit,
hedge funds, and high-frequency market making and
trading. The growth of NBFI can also strengthen
prospects for financial stability. A broader set of finan-
cial intermediaries with different risk profiles, time
horizons, and expertise avoids overreliance on banks,
increases competition, provides diversification to bor-
rowers and investors, and creates mechanisms for risk

x

International Monetary Fund | October 2024

transfer away from the banking system. These benefits,
however, are unevenly distributed across countries at
the moment, with many advanced, emerging market,
and developing economies remaining bank-centric and
standing to benefit from further development of NBFI
and market-based finance.

While these benefits are likely sizable, reaping them
requires policy steps to contain risks to global financial
stability. Past GFSRs, for instance, have discussed vul-
nerabilities arising from liquidity mismatches in open-
ended mutual funds, highly leveraged trading strategies
used by hedge funds, and opaque interconnectedness
in the broad NBFI sector. The GFSR has also shown
that, as nonbank financial institutions become vital to
intermediation in core financial markets—such as gov-
ernment and corporate bonds—the availability of mar-
ket liquidity in times of stress has come into question.
Recent episodes of stress have required central banks
to intervene. International standard setters are making
progress in enhancing NBFI resilience. It is paramount
that these international policy initiatives continue
expeditiously and that national authorities implement
them in a timely and consistent manner.

Enhancing the resilience of the financial sector is
particularly relevant in the current context, where both
economic and policy uncertainty are elevated against
a backdrop of rising geopolitical risks. As shown in
Chapter 2, economic uncertainty increases downside
risks to future growth, asset prices, and bank lending.
Additionally, uncertainty can trigger cross-border spill-
over effects through trade and financial linkages.

Thinking Through the “Future of
Finance”

With the financial sector continuing to transform,
it is imperative that policymakers think through the
“future of finance.” Technological innovation in finan-
cial activities can increase efficiency and competition,
while increasingly disrupting financial services tradi-
tionally provided by banks. Novel lending modalities
for private credit are likely to continue growing,

artificial intelligence may support further growth in
NBFI (see Chapter 3 of the GFSR), and digital banks
are growing in systemic importance.

Policymakers must ensure that the balance between

benefits and risks in this new state of financial
intermediation remains appropriate. Traditionally,
prudential regulation of nonbanks tends to be either
absent or less strict because they do not take deposits
from retail investors and largely do not have recourse
to central bank backstops. However, with the growth
in the relative size of NBFI and its close linkage with
the banking sector, more substantive externalities
may be generated, potentially requiring novel pol-
icy approaches. The regulatory framework needs to
be proportionate to the systemwide risks posed by
different institutions and acknowledge that risk-taking
is needed for financial intermediation, as someone—
not only central banks—should be there to “catch
the falling knife” during stress times. However, policy
must weigh the costs and benefits of such risk-taking
carefully, and with broad financial stability objectives
in mind. The discussion of the August 2024 turmoil
in this edition of the GFSR is a recent illustration of
this challenge. When volatility spiked, many leveraged
investors reached risk limits and received increased
margin calls, which forced them to rapidly close
their positions. Such practices helped protect indi-
vidual institutions from a potential worsening of the
turbulence but also caused nonlinear effects that likely
exacerbated the sell-off. Therefore, regulation and
supervision of NBFI also need to consider broader
financial stability objectives.

Making the most of the ongoing transformation and

ensuring that the financial sector can sustainably fuel
economic growth requires further progress on several
fronts:
 • Expand data collection: Regulators need to collect
more comprehensive data on NBFIs that allows
a better evaluation of the risks to global financial
stability and a more complete map of the interlinks
of the sector. Information on the use of leverage and
asset holdings will allow the development of more
effective policies to address systemic risks while
avoiding stifling financial innovation.

 • Increase transparency: Nonbanks tend to be

relatively opaque and often do not provide enough
information to investors and the public. The
growing potential for spillovers from NBFIs to
the broader financial system and the increasing

FOREWORd

participation of retail investors require more trans-
parency. Conduct requirements, including public
disclosure, are also important to support market
discipline and price discovery.

 • Design appropriate liquidity facilities and

backstops: Liquidity stress in the NBFI sector can
spill over to the broader financial sector—as seen
during stress episodes such as the March 2020
dash-for-cash—and eventually to the real economy.
In such circumstances, central banks may face a
trade-off between providing support to NBFI—and
therefore safeguarding financial stability in the
short term—and introducing moral hazard whereby
NBFIs can rely on central banks to resolve the
fallout from excessive risk-taking. If market stress
occurs during periods of high inflation and mone-
tary policy tightening, another trade-off could also
arise between the central bank mandates of financial
and price stability. Consequently, it is necessary to
develop mechanisms for central bank support that
minimizes moral hazard and encourages nonbanks
to internalize liquidity risks. Communication plans
that avoid central banks being perceived as working
at cross purposes, such as purchasing assets to restore
financial stability while tightening monetary policy
to fight inflation, are also critical.

 • Improve the financial “plumbing”: A diverse

financial system requires an underlying “plumbing”
structure that allows for the smooth movement of
money and assets. Maintaining trust in the system
requires ensuring that financial transactions are
safely completed even in times of stress. It is there-
fore key to ensure that payments and settlements
systems work effectively and securely. In this regard,
it is first order to continue working toward ensuring
interoperability (for example, that different systems
and platforms work together seamlessly, especially
on a cross-border basis) and integrating new tech-
nologies including artificial intelligence to enhance
efficiency and security.

 • Enhance the resilience of central counterparties:

The international regulatory reform agenda after the
global financial crisis called for the use of central
counterparties (CCPs) for clearing certain types
of derivatives. The shift toward CCPs created a
more robust and transparent financial system but
also increased their systemic importance, making it
paramount to enhance their resilience. This requires
ensuring that CCPs have enough resources to cover

International Monetary Fund | October 2024

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GLOBAL FINANCIAL STABILITY REPORT: STEAdYING ThE COuRSE: uNCERTAINTY, ARTIFICIAL INTELLIGENCE, ANd FINANCIAL STABILITY

potential losses, business continuity plans are in
place, and recovery and resolution plans clearly
outline the measures a CCP will take to restore
its stability or wind down its operations if it fails.
Margining requirements also need to be reviewed
aiming not only to protect the CCP but also to
consider the impact that margin and collateral calls
during times of stress might have in the broader
system.

 • Undertake a systemic approach to enhancing
resilience of NBFI: Vulnerabilities in the NBFI
sector can amplify shocks and have systemic impli-
cations. For instance, liquidity mismatches in bond
funds may add selling pressure in times of stress;
significant increases in margin calls in the derivatives
market may create systemic liquidity stress during
times of elevated market volatility; and the failure
of highly leveraged nonbanks, such as in the case
of Archegos, might generate substantial losses for
banks. Despite the potential for significant negative
externalities from NBFIs, the prudential framework
for NBFI in most countries is focused on specific
institutions and sectors and does not include system-
wide and cross-sectoral perspectives. The absence

of a macroprudential perspective for nonbanks
means that, during good times, risks in the NBFI
sector can grow and create externalities throughout
the financial system. It is necessary for the relevant
authorities to coordinate more closely in order to
ensure that sound governance structures, mech-
anisms, and processes to monitor NBFIs from a
systemic perspective are in place. It is also necessary
to sharpen existing tools and potentially develop
new ones to address potential systemic risk.

This is a long and challenging list. In summary, the
ask is to enhance the prudential framework to address
systemic risks from a larger NBFI sector while also
considering that leverage facilitates financial intermedi-
ation and that interconnectedness means more efficient
capital mobility and allocation, which is the core role
of the financial sector. The IMF is ready to continue
working with its member authorities, other interna-
tional finance institutions, and global standard-setting
bodies to achieve these goals.

Tobias Adrian
Financial Counsellor

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International Monetary Fund | October 2024

EXECUTIVE SUMMARY

Steadying the Course: Financial Markets
Navigate Uncertainty

Since the April 2024 Global Financial Stability Report, global

economic activity has moderated, and inflation has continued
to slow. With monetary easing under way among major central
banks, financial conditions have remained accommodative,
emerging markets have remained resilient, and asset price
volatility has stayed relatively low, on net. Near-term financial
stability risks, according to the IMF’s one-year-ahead growth-
at-risk measure, remain contained at around the 40th histor-
ical percentile. However, accommodative financial conditions
that keep near-term risks at bay also facilitate the buildup of
vulnerabilities—such as lofty asset valuations, the global rise in
private and government debt (Figure ES.1), and increased use of
leverage by nonbank financial institutions—which raises risks to
financial stability in the future.

These mounting vulnerabilities could amplify adverse shocks,

which have become more probable due to elevated economic
and geopolitical uncertainty amid ongoing military conflicts
and the uncertain future policies of newly elected governments.
In particular, the widening disconnect between uncertainty and
market volatility (Figure ES.2) increases the chance of sudden
surges in volatility and sharp asset repricing, which could be
amplified by the vulnerabilities. As shown in Chapter 1, the
market turmoil in early August 2024—when stock market vola-
tility spiked in both Japan and United States (Figure ES.3) and
global asset prices declined significantly—provided a glimpse
of the violent reactions that can ensue when spikes in volatil-
ity interact with the use of leverage by financial institutions to
create nonlinear market reactions and hasten sell-offs.

Indeed, Chapter 2 quantitatively demonstrates that further
rises in economic uncertainty could increase downside risks to
future growth, asset prices, and growth in bank lending. For
example, assuming global real economic uncertainty jumps by
an amount equivalent to its rise during the global financial crisis,
the downside outcome (specifically, the 10th percentile) of one-
year-ahead global real GDP growth worsens by 1.2 percentage
points (Figure ES.4). This effect is stronger when macrofinancial
vulnerabilities are more elevated or when market volatility is
more disconnected from uncertainty. Uncertainty can also trigger
cross-border spillover effects through trade and financial linkages.
This Global Financial Stability Report delves into the financial

vulnerabilities and imbalances challenging financial stability,
highlighting the urgency for policymakers to address them.

Figure ES.1. Aggregate Debt-to-GDP Ratios
(Historical z-scores)

250

200

150

100

50

Emerging Markets

Projections

Private nonfinancial

0
1998 2000 02 04 06 08 10

Government

Advanced Economies

12

14

16

18

20 22

24

26 28

Projections

300

250

200

150

100

50

Private nonfinancial

Government

0
1998 2000 02 04 06 08 10

12

14

16

18

20 22

24

26 28

Sources: Bank for International Settlements; and IMF staﬀ calculations.

Note: Dashed green lines are government debt-to-GDP ratio projects based on the
IMF’s Global Debt Database.

Figure ES.2. Diﬀerence of the Standardized Measures of
Financial Volatility, Economic Uncertainty, and Geopolitical
Risk
(Historical z-scores)

Financial volatility minus economic uncertainty
Financial volatility minus geopolitical risk

2.5

2.0

1.5

1.0

0.5

0

−0.5

−1.0

−1.5

−2.0

2013

14

15

16

17

18

19

20

21

22

23

24

Source: Bloomberg Finance L.P.
Note: “Economic uncertainty” is from Baker, Bloom, and Davis (2016); “financial
volatility” is the average of the Chicago Board Options Exchange Volatility Index (VIX),
High-Yield Corporate VIX, and Currency VIX; “geopolitical risk” is from Caldara and
Iacoviello (2022). Figure shows 12-month moving average values of the diﬀerences in
z-scores.

Figure ES.3. Equity Market Volatility in Japan and the
United States
(Percent)

VIX Index
Nikkei Volatility Index

April 2024
GFSR

August 5

80

70

60

50

40

30

20

10

Jan.
2023

Apr.
23

Jul.
23

Oct.
23

Jan.
24

Apr.
24

Jul.
24

Sources: Bloomberg Finance L.P.; and IMF staﬀ calculations.
Note: The Chicago Board Options Exchange Volatility Index, or VIX, is the benchmark
measure of US stock market volatility, based on S&P 500 options. Its Japanese
counterpart is the Nikkei Stock Average Volatility Index.

International Monetary Fund | October 2024

xiii

GLOBAL FINANCIAL STABILITY REPORT: STEAdYING ThE COuRSE: uNCERTAINTY, ARTIFICIAL INTELLIGENCE, ANd FINANCIAL STABILITY

Figure ES.4. Eﬀect of Real Economic Uncertainty on
Growth-at-Risk by Horizon
(Percentage points, annualized)

0.5

0.0

−0.5

−1.0

−1.5

−2.0

−2.5

1

2

3

4

5

6
7
Quarters

8

9

10

11

12

Sources: IMF, Global Debt and International Financial Statistics databases;
Organisation for Economic Co-operation and Development, Main Economic
Indicators database; and IMF staﬀ calculations.

Note: The figure shows the average eﬀect of a one-standard-deviation increase in a
real economic uncertainty index on the 10th percentile of the future real GDP growth
distribution for a panel of advanced and emerging market economies. See Chapter 2
for a more detailed description.

Figure ES.5. Spillover to Emerging Market Term Premiums
From US Term Premiums
(Percent)

LATAM

CEEMEA

Asia

Average

35

30

25

20

15

10

5

0
2009

11

13

15

17

19

21

23

Sources: Based on Diebold and Yilmaz 2009; Bloomberg Finance L.P.; and IMF staﬀ
calculations.
Note: The figure shows the proportion of variation in emerging market term
premiums explained by shocks to US term premiums (TPs). CEEMEA = Central and
Eastern Europe, the Middle East, and Africa; EMs = emerging markets; LATAM =
Latin America.

Figure ES.6. Global Issuance of Sustainable Debt
(Billions of US dollars, left scale; percent, right scale)

Sustainability-linked loan
Sustainability bond
Green loan
Share of EMs in total (right scale)

Sustainability-linked bond
Social bond
Green bond

s
r
a
l
l
o
d
S
U

f
o
s
n
o
i
l
l
i

B

500

400

300

200

100

0

20

16

12

8

4

t
n
e
c
r
e
P

2019:
Q1

19:
Q3

20:
Q1

20:
Q3

21:
Q1

21:
Q3

22:
Q1

22:
Q3

23:
Q1

23:
Q3

24:
Q1

0

24:Q3
(norm)

Sources: Bloomberg Finance L.P.; Bloomberg NEF; and IMF staﬀ calculations.

Note: The share of EMs shows the four-quarter moving average of total issuance in
EMs as a percentage of global issuance, and the Q3 2024 value is based on the latest
available information as of August 2024. “24:Q3 (norm)” refers to the normalized
value for Q3 2024, based on issuance during July–August 2024.

xiv

International Monetary Fund | October 2024

Vulnerabilities and Imbalances

High levels and rapid growth of sovereign debt remain a
global challenge, with many jurisdictions failing to achieve
their longer-term debt-stabilizing primary balances. In many
advanced economies, increasingly large shares of issuances of
government debt will need to be absorbed by price-sensitive
buyers amid ongoing quantitative tightening by their central
banks, potentially increasing bond market volatility. Emerging
markets and frontier economies with weak and worsening fiscal
buffers have seen their sovereign bond and credit default swap
spreads increase more than those of other jurisdictions, making
debt servicing more challenging.

Emerging markets have continued to demonstrate resil-
ience since the April 2024 Global Financial Stability Report.
Central banks have remained focused on domestic economic
and inflation conditions in setting monetary policy, relying on
adjustments in exchange rates to mitigate external headwinds.
With major advanced economies set to ease monetary policy,
pressure on emerging markets could moderate in the near term.
Further ahead, however, elevated uncertainty regarding trade
policies and geopolitics and a slowing growth outlook in China,
a key trading partner for many emerging markets, could make
preserving financial stability in emerging markets more chal-
lenging. Portfolio flows may become more volatile and access to
international funding may be more difficult, especially for fron-
tier economies. Indeed, interest rate spillovers from advanced
economies to emerging markets have increased over the past
decade, as changes in the 10-year US term premium—risk
premiums investors demand to hold longer-term securities—
have explained an increasing share of the changes in the term
premiums of 10-year emerging market bonds (Figure ES.5).

Global issuance of sustainable debt has rebounded in 2024.
However, emerging markets account for just 13 percent of year-
to-date issuance (Figure ES.6), and the share of emerging mar-
ket sustainable debt denominated in local currencies is small.
Underinvestment in climate finance could delay mitigation and
adaptation efforts and could challenge financial stability in the
future.

Even if global interest rates are declining, many firms would
find debt servicing a challenge in coming years. Although solid
economic activity and healthy corporate balance sheets have
kept margins robust for some firms, defaults have steadily risen
as weaker firms have struggled. Some midsized companies
borrowing at high interest rates in private credit markets are
becoming increasingly strained and have resorted to payment-
in-kind methods, effectively deferring interest payments and
piling on more debt. In addition, trade restrictions and geo-
political events are likely to affect corporations through higher

input costs and a reduced capacity to make interest payments.
Despite mounting signs of credit deterioration, corporate
bonds have continued to trade within tight spreads by histor-
ical standards, leading to pricing misalignments that indicate
an increased risk of an abrupt repricing of credit risk.

Although stability risks from residential real estate appear

contained in most countries, pressures on the commercial
real estate (CRE) sector remain acute. Misalignment in
prices and fundamentals point to further corrections in the
CRE market, especially the embattled office sector. Funding
could be withdrawn, pushing down prices and putting more
financial institutions under pressure in an adverse feedback
loop (Figure ES.7). Both banks with outsized concentrations
in CRE and nonbank investors such as real estate investment
trusts may experience strains.

The global banking sector has remained resilient, with
ample capital and liquidity buffers. Although nonperforming
loan ratios have increased for some forms of lending, such as
consumer credit cards, automobile loans, and CRE, overall
asset quality has not deteriorated significantly. However, net
interest margin and bank profitability could be negatively
impacted by interest rate cuts, and the temporary sell-off of
some banks’ stocks in early August highlighted some of the
risks ahead, particularly for a relatively large tail of weaker
institutions facing challenges related to their business models
(Figure ES.8).

The market turmoil in early August serves as another
example of how nonbank financial intermediations (NBFIs)
can transmit strains through the financial system and amplify
stress, as the rapid unwinding of leveraged positions can
generate liquidity imbalances that increase volatility. With the
growth of open-ended bond funds, hedge funds, and private
credit, the use of leverage among several NBFI segments is
increasing. Data gaps, which hinder authorities’ ability to
assess the vulnerabilities associated with nonbank leverage and
to identify large and concentrated positions, present a key
challenge in addressing these issues.

Use of artificial intelligence (AI) in capital market activities

may further support the growth of NBFIs. Chapter 3 shows
that although adoption of AI in trading and investment activ-
ities is still at a relatively early stage, it could accelerate in the
coming years: For example, the share of applications related to
AI and machine learning in patent filings in asset management
has risen impressively in recent years (Figure ES.9). Although
adopting these new technologies may bring efficiencies and
cost savings to both banks and NBFIs, the latter are gener-
ally more agile and subject to fewer constraints in using AI.
Indeed, NBFIs are already dominating several asset markets

ExECuTIvE SuMMARY

Figure ES.7. Maturing Commercial Real Estate Debt and
Funding Gap
(Billions of US dollars)

1,600

1,400

1,200

1,000

800

600

400

200

0

US
Europe
APAC

Oﬃce
Retail
Industrial
Multifamily

2024

25

26

27

28

USA

Europe

400

300

200

100

0

p
a
g
g
n
d
n
u
f

i

t
b
e
D

Sources: AEW; EUROPACE AG/Haver Analytics; Trepp; and IMF staﬀ calculations.
Note: APAC = Asia and Pacific.

Figure ES.8. Banks Signaling Vulnerabilities in Three or
More Areas of Risk
(Total assets, trillions of US dollars)

Assets: 4+ flags
Forecast
Num. banks: 4+ flags

Assets: 3 flags
Forecast
Num. banks: 3 flags

Forecast
period

s
t
e
s
s
A

50
45
40
35
30
25
20
15
10
5
0

2
Q
:
8
1

3
Q
:
8
1

4
Q
:
8
1

1
Q
:
8
1
0
2

1
Q
:
9
1

2
Q
:
9
1

3
Q
:
9
1

4
Q
:
9
1

1
Q
:
0
2

2
Q
:
0
2

3
Q
:
0
2

4
Q
:
0
2

1
Q
:
1
2

2
Q
:
1
2

3
Q
:
1
2

4
Q
:
1
2

1
Q
:
2
2

2
Q
:
2
2

3
Q
:
2
2

140

120

100

80

60

40

20

0

s
k
n
a
b
f
o
r
e
b
m
u
N

4
Q
:
4
2

1
Q
:
4
2

2
Q
:
4
2

3
Q
:
4
2

4
Q
:
2
2

1
Q
:
3
2

2
Q
:
3
2

3
Q
:
3
2

4
Q
:
3
2

Sources: Visible Alpha; and IMF staﬀ calculations.
Note: Forecast period is based on aggregate consensus analysts’ forecasts. Num. =
number of.

Figure ES.9. Patents in Asset Management Related to
Artificial Intelligence
(Number; percent, right scale)

210

180

150

120

90

60

30

0

AA/PM
Of which contains AI/ML term
AI/ML-share (right scale)

2009 10

11 12 13 14 15 16 17 18 19 20 21 22 23

80

70

60

50

40

30

20

10

0

Sources: World Intellectual Property Organization, PATENTSCOPE database; and IMF
staﬀ calculations.
Note: Aggregate patents may not be an exhaustive accounting of all patents filed
with national authorities and are limited to those available in the PATENTSCOPE
database. See Chapter 3 for details. AA = asset allocation; AI = artificial intelligence;
ML = machine learning; PM = portfolio management.

International Monetary Fund | October 2024

xv

GLOBAL FINANCIAL STABILITY REPORT: STEAdYING ThE COuRSE: uNCERTAINTY, ARTIFICIAL INTELLIGENCE, ANd FINANCIAL STABILITY

amenable to electronic trading. Widespread adoption
could also worsen financial fragilities in the future,
through potentially higher volatility during market
stress, more opacity, and challenges in monitoring how
AI is used in capital markets and by whom, reliance
on a few key AI service providers increasing opera-
tional risks, and growing risks of cyber and market
manipulation.

Policy Recommendations

Although near-term financial stability risks appear
contained, vulnerabilities are mounting in the finan-
cial system. Now is the time for policymakers to act
to limit fragilities. For central banks, clear communi-
cations that the path of monetary policy should not
react excessively to any individual data point would
help reduce uncertainty. Where growth and inflation
momentum are set to continue, central banks should
gradually ease monetary policy toward a more neutral
stance. Where inflation remains stubbornly above
targets, central banks should push back against overly
optimistic investor expectations for monetary policy
easing.

With sovereign debt in many countries substantially

above prepandemic levels, fiscal adjustments should
focus primarily on credibly rebuilding buffers to keep
financing costs reasonable, help anchor medium-term
inflation expectations, and contain risks of sovereign
rating downgrades. For countries with less fiscal space,
the credibility of fiscal plans is imperative to prevent
cliff effects in ratings that could adversely affect financ-
ing conditions. Sovereign borrowers in frontier econ-
omies and low-income countries should strengthen
efforts to contain risks associated with their debt
vulnerability through, among other measures, commu-
nications with creditors, multilateral cooperation, and
support from the international community.

With the continued growth of NBFIs, from open-
ended investment funds to hedge funds and private
funds, the risks from increased maturity mismatches

and leverage underscore the need for more active
regulatory and supervisory engagement. It is crucial
to enhance reporting requirements for NBFIs and to
strengthen policies that mitigate vulnerabilities and
mechanisms of amplification stemming from nonbank
leverage. Improving NBFIs’ liquidity preparedness,
implementing the Financial Stability Board’s agreed-
upon standards, and enhancing stress testing for
nonbanks could reduce systemic risks.

Continued buildup of debt and elevated economic

uncertainty underscore the need to strengthen the
macroprudential policy framework to contain exces-
sive risk taking in the nonbank financial sector and
to ensure that capital and liquidity buffers in banking
systems are adequate to support the provision of credit
through periods of stress. Policymakers should tighten
macroprudential tools to increase resilience against a
range of shocks while avoiding a broad tightening of
financial conditions.

Continued vigilance is warranted to monitor vulner-
abilities of corporations and the CRE sector. To ensure
resilience within the banking system and to inform
decisions regarding capital adequacy, authorities should
conduct stress-testing exercises that incorporate scenar-
ios involving trade restrictions, geopolitical events, and
significant declines in CRE prices. Given the increas-
ingly significant role of private credit in financial
markets, enhancing reporting requirements to improve
monitoring and management of risks is imperative.

The tail of weak banks in the global financial system
and the risk of contagion to healthy institutions under-
score the need to be ready to address financial instabil-
ity. It is crucial to ensure that supervisors are equipped
to intervene early and that banks are prepared to access
central bank liquidity. Further progress on adopting
and implementing frameworks for recovery and reso-
lution is critical for addressing weak or failing banks
without undermining financial stability or risking
public funds. Full, timely, and consistent implemen-
tation of international standards remains important in
enhancing prudential frameworks.

xvi

International Monetary Fund | October 2024

IMF EXECUTIVE BOARD DISCUSSION OF THE OUTLOOK,
OCTOBER 2024

The following remarks were made by the Chair at the conclusion of the Executive Board’s discussion of the
Fiscal Monitor, Global Financial Stability Report, and World Economic Outlook on October 8, 2024.

E

xecutive Directors broadly agreed with staff ’s
assessment of the global economic outlook,
risks, and policy priorities. They welcomed
the continued growth resilience of the global

economy in the face of recurring shocks. Directors
highlighted that monetary policy has managed to bring
about disinflation with so-far limited cost to output
and employment, increasing the likelihood of a smooth
landing. They noted, however, that the recovery
remains uneven and that growth, while steady, remains
underwhelming, reflecting weak productivity growth.
They noted that mediocre medium-term growth and
rising debt trajectories increase the risk that the global
economy will become entrenched in a low-growth,
high-debt environment. Against this backdrop, they
agreed that, as monetary policy becomes less restrictive,
a renewed emphasis on gradual and sustained fiscal
consolidation, coupled with ambitious structural
reforms, is needed, with due regard for country-specific
conditions.

While most Directors agreed that risks to the
outlook are now tilted to the downside, a number
of Directors also cautioned against overstating the
deterioration in the balance of risks. Directors
noted, in particular, risks from potentially more
persistent underlying inflation, increased geopolitical
conflicts and tensions in different regions, and the
intensification of protectionist policies that could
weigh down on medium-term growth. Directors
noted that while the monetary easing underway has
helped keep financial conditions accommodative and
near-term financial stability risks at bay, this may in
turn facilitate the buildup of financial vulnerabilities.
They stressed that the widening disconnect between
subdued financial market volatility, relative to elevated
economic and geopolitical uncertainty, increases the
chances of sharp disorderly repricing. Further volatility
surges could impair financial stability as well as

investment and growth, especially in emerging market
and developing economies heavily reliant on external
financing. Directors also noted still-acute pressures on
commercial real estate sectors and ongoing property
sector adjustments in some countries. Some Directors
highlighted upside risks to the outlook, including
a stronger recovery in investment in advanced
economies, better performance in some emerging
market economies, and economic benefits from
artificial intelligence.

Directors called on central banks to carefully
calibrate monetary policy to restore price stability,
avoiding a tighter-than-necessary stance that could
weaken growth and employment. They emphasized
the importance of remaining data dependent and
clearly communicating policy decisions. Directors
stressed that, in economies where core inflation persists
at above-target levels, policy rates should remain in
restrictive territory until underlying inflation shows
clear signs of moving toward target. They agreed that
moving to a more neutral stance is appropriate in
economies where inflation is unambiguously abating,
long-term inflation expectations remain anchored, and
output gaps are closing. Given elevated economic and
policy uncertainty, Directors called on central banks
to stand ready to mitigate the potential disruptive
impacts of foreign exchange volatility and capital
flows, including by leveraging, where appropriate,
the country-specific guidance provided by the IMF’s
Integrated Policy Framework.

Directors welcomed that the global banking sector

has remained resilient and emphasized that further
progress on adopting and implementing frameworks for
recovery and resolution is critical for addressing weak
or failing banks. They concurred that full, timely, and
consistent implementation of international standards,
including Basel III, remains important to enhance
prudential frameworks. Directors stressed the need

International Monetary Fund | October 2024

xvii

GLOBAL FINANCIAL STABILITY REPORT: STEAdYING ThE COuRSE: uNCERTAINTY, ARTIFICIAL INTELLIGENCE, ANd FINANCIAL STABILITY

to improve non-bank financial institutions’ liquidity
preparedness, implement the Financial Stability Board’s
agreed-upon standards, close data gaps, and enhance
stress testing for non-banks to reduce systemic risks.
Directors generally called for sustained, gradual,

and carefully designed fiscal adjustments amid
elevated public debt and associated risks. They noted
that larger adjustments than currently envisaged
in many countries are needed to stabilize debt
and build necessary buffers against adverse shocks.
Directors stressed that the pace of adjustment
should be calibrated to country-specific economic
conditions, should ensure continuous support to
the most vulnerable and protect public investment,
and should be well communicated and anchored in
credible medium-term frameworks. They stressed
that strengthening fiscal governance should be a
priority and would help reduce the debt buildup from
contingent liabilities and arrears.

Directors stressed the importance of advancing
structural reforms to boost growth and accelerate
the green transition, noting the need to enhance the
social acceptability of these reforms through enhanced
communication and trust-building mechanisms.
They emphasized that targeted reforms are needed
to boost productivity, enhance competition, improve
human capital, and increase labor force participation.
Directors reiterated the need to advance with climate
mitigation and adaptation reforms. In this context,
some Directors emphasized the need to strengthen
efforts to increase climate finance for adaptation,
especially for vulnerable countries exposed to
significant climate risks.

Directors underscored that stronger multilateral
cooperation is essential to facilitate debt restructuring
processes, mitigate risks from geoeconomic
fragmentation, and accelerate the green transition in a
manner consistent with World Trade Organization rules.

xviii

International Monetary Fund | October 2024

STEADYING THE COURSE: FINANCIAL MARKETS
NAVIGATE UNCERTAINTY

Chapter 1 at a Glance
 • Since the April 2024 Global Financial Stability Report, near-term financial stability risks have remained

contained. Global economic activity has moderated, inflation has slowed, emerging markets have
remained resilient, financial conditions have remained accommodative, and volatility in financial markets
has remained low, on net.

 • However, accommodative financial conditions facilitate the further buildup of vulnerabilities. Asset valua-
tions appear lofty, debt has climbed globally, and the use of leverage among nonbank financial intermedi-
aries has increased. Fragilities in corporate and commercial real estate sectors remain.

 • These imbalances could worsen future downside risks by amplifying adverse shocks, which have become
more probable due to the widening disconnect between elevated economic uncertainty—stemming from
ongoing military conflicts and the uncertain future policies of newly elected governments—and low finan-
cial volatility. Market turmoil in early August, though short-lived, served as a reminder of how quickly
volatility can catch up to uncertainty, force the unwinding of leveraged trades, and trigger feedback loops
between asset prices and deleveraging.

 • Certain types of nonbank financial intermediaries amplified the early August turmoil and warrant more
active supervisory engagement. The banking system has remained sound, although a weak tail of banks
is still confronting exposures to troubled sectors like commercial real estate and ongoing business model
challenges.

 • Emerging markets have broadly demonstrated continued resilience, but preserving financial stability could
be more challenging going forward. The slowing growth outlook in China and fragilities in its financial
system are a key downside risk to the global economy. Access to funding for frontier markets and econo-
mies with weaker fiscal buffers may become more constrained. Underinvestment in climate finance would
delay climate mitigation and adaptation in emerging markets and developing economies, with financial
stability implications to come.

Policies to Address Financial Vulnerabilities
 • For central banks, clear communications that the path of monetary policy should not react excessively to
any individual data point would help reduce uncertainty. Where growth and inflation momentum are set
to continue, central banks should gradually ease monetary policy toward a more neutral stance. Where
inflation remains stubbornly above targets, central banks should push back against overly optimistic inves-
tor expectations for monetary policy easing that would further stretch asset prices.

 • With levels of sovereign debt in many advanced and emerging market economies substantially above

prepandemic levels, fiscal adjustments should primarily focus on credibly rebuilding buffers to keep exter-
nal financing costs reasonable and to help anchor medium-term inflation expectations. Sovereign borrow-
ers in frontier economies and low-income countries should strengthen efforts to contain risks associated
with high levels of debt vulnerability.

 • Policies that address nonbank leverage and liquidity mismatches need to be strengthened. Renewed efforts
to implement internationally agreed-upon bank prudential standards in a timely and consistent manner
would reduce opportunities for regulatory arbitrage across borders and sectors.

 • Authorities should expand recovery and resolution plans, ensure that financial institutions are prepared
to access central bank liquidity, and intervene early to prevent future strains in the financial sector from
turning systemic.

International Monetary Fund | October 2024

1

CHAPTER1GLOBAL FINANCIAL STABILITY REPORT: STEAdYING ThE COuRSE: uNCERTAINTY, ARTIFICIAL INTELLIGENCE, ANd FINANCIAL STABILITY

Introduction

Since the April 2024 Global Financial Stability Report,

global economic activity has moderated, and infla-
tion has continued to slow. With major central banks
undertaking monetary easing and modest risks of an
imminent global recession (see the October 2024 World
Economic Outlook), asset prices have stayed buoyant and
financial conditions accommodative. Major emerging
markets have remained resilient and have continued
the proactive policymaking that has helped mitigate the
multitude of shocks since the COVID-19 pandemic.
Near-term risks to financial stability, according to the
IMF’s Growth-at-Risk (GaR) model, have remained
contained at around 40th historical percentile.

However, accommodative financial conditions could

prompt a further buildup of several vulnerabilities
that worsen downside risks in the future. First, asset
valuations appear lofty in equity and corporate credit
markets, driven by buoyant investor sentiment seem-
ingly undeterred by a slowdown in earnings growth of
firms and the continued deterioration in more fragile
segments of the corporate and commercial real estate
(CRE) sectors. Second, government debt continues to
mount, a consequence of still-expansionary fiscal policies
in many countries (see the October 2024 Fiscal Mon-
itor). A number of advanced economies are increasing
the issuances of government bonds while central banks
are conducting quantitative tightening, portending
larger swings in bond yields. In emerging markets, sov-
ereign credit spreads have become sensitive to countries’
fiscal buffers, and certain weaker jurisdictions may have
trouble refinancing debt maturing on the horizon at
sustainable interest rates. Third, the use of leverage by
financial institutions, especially by nonbank financial
intermediation (NBFI) like hedge funds and private
credit funds, have risen; maturity mismatches at some
open-ended funds and insurers have widened.

These imbalances could worsen future financial
stability risks by amplifying adverse shocks, which have
become more probable due to elevated economic and
geopolitical uncertainty. Much of this uncertainty is
because half of the world’s population has elected or will
elect new governments this year, and future policies that
these governments will enact—ranging from fiscal to
trade to geopolitical—are in many cases difficult to pin
down. Heightened uncertainty may also reflect the unpre-
dictability of ongoing military conflicts, notably in the
Middle East and in Ukraine. Adverse shocks are not only
more probable; the widening disconnect between uncer-
tainty and relatively low volatility in financial markets

2

International Monetary Fund | October 2024

suggests that they could trigger a spike in volatility, bring-
ing it in line with prevailing uncertainty.1 This could raise
value-at-risk measures, bind risk limits, and trigger margin
calls, practices that can protect individual institutions
from turbulent markets but may also cause nonlinear
effects that hasten sell-offs.2 For example, broker–dealers
may find their balance sheets constrained by risk limits in
volatile markets, curtailing their intermediation capacities
(see FSB 2017; Adrian, Boyarchenko, and Shachar 2017),
while NBFIs facing margin calls might be forced to
deleverage by selling assets into a falling market.

The severe, albeit short-lived, market turmoil in
early August provided a glimpse of the violent reac-
tions markets can incur when volatility catches up to
uncertainty. Global stock prices fell sharply (the Nikkei
index declined by 12 percent on August 5)—what
began as investors’ unwinding of carry trades that
borrowed yen to fund long positions in global risk
assets was amplified by selling of risk assets following
the Bank of Japan’s monetary policy decision in late
July and a weaker-than-expected July US labor market
report. Equity volatility surged from compressed levels,
contributing to further sell-offs (see Box 1.3) before
subsiding over subsequent days.

Looking ahead, the uncertainty-volatility disconnect

may increase downside risks to growth, as quantified
in Chapter 2. When shocks arrive and volatility rises,
hedge funds may further unwind leveraged positions,
and algorithmic traders—which have gained significant
market shares in various asset classes—may sell in fall-
ing markets to protect themselves against further losses,
exacerbating price declines. Recent advancements in
artificial intelligence and machine learning suggest that
algorithms may play a larger role in future episodes of
turbulence, as discussed in Chapter 3.

Emerging markets have continued to demonstrate
resilience since the April 2024 Global Financial Stability
Report, notably against pressures on their currencies. A
number of major emerging market central banks remain
focused on domestic economic and inflation conditions

1The asset price literature generally predicts a close link between

uncertainty about economic growth and volatility of asset prices.
See, for example, the seminal paper of Lucas (1978), in which asset
prices and their returns are more volatile the higher the variance of
consumption growth because the stochastic discount factor for asset
prices is a function of intertemporal marginal utility from consump-
tion. Deviations between the two are explained by the presence of risk
premiums in financial markets. The current gap between uncertainty
and volatility indicates low risk premiums and investor complacency.

2See Abboud and others (2021) for an example of value-at-risk rising

with market volatility and Brunnermeier and Pedersen (2009) for a
model of the procyclicality between margin requirements and volatility.

CHAPTER 1

STEAdYING ThE COuRSE: FINANCIAL MARkETS NAvIGATE uNCERTAINTY

Figure 1.1. Market-Implied Expectations of Policy Rates for Selected Advanced and Emerging Market Economies
(Percent)

Market pricing suggests most major central banks will cut policy rates this year.

1. Federal Reserve

2. European Central Bank

3. Bank of England

4. Bank of Japan

5.5

5.0

4.5

4.0

3.5

3.0

2.5

2.0

1.5

Current
Past GFSR

Current
Past GFSR

4
2

.
v
o
N

5
2

.
r
p
A

5
2

.

p
e
S

6
2

.

b
e
F

6
2

.
l

u
J

6
2

.
c
e
D

7
2
y
a
M

7
2

.
t
c
O

4
2
0
2

.

n
u
J

4
2

.
v
o
N

5
2

.
r
p
A

5
2

.

p
e
S

6
2

.

b
e
F

6
2

.
l

u
J

6
2

.
c
e
D

7
2
y
a
M

7
2

.
t
c
O

4
2
0
2

.

n
u
J

5.5

5.0

4.5

4.0

3.5

3.0

2.5

2.0

1.5

Current
Past GFSR

4
2

.
v
o
N

5
2

.
r
p
A

5
2

.

p
e
S

6
2

.

b
e
F

6
2

.
l

u
J

6
2

.
c
e
D

7
2
y
a
M

7
2

.
t
c
O

4
2
0
2

.

n
u
J

5.5

5.0

4.5

4.0

3.5

3.0

2.5

2.0

1.5

Current
Past GFSR

5
2

.
r
a
M

5
2

.

p
e
S

6
2

.
r
a
M

6
2

.

p
e
S

7
2

.
r
a
M

7
2

.

p
e
S

4
2
0
2

.

p
e
S

1.0

0.8

0.6

0.4

0.2

0

5. Central Bank of Brazil

6. National Bank of Poland

7. Reserve Bank of India

8. Bank of Mexico

14

13

12

11

10

9

Current
Past GFSR

Current
Past GFSR

4
2

.
c
e
D

5
2

.

n
u
J

5
2

.
c
e
D

6
2

.

n
u
J

6
2

.
c
e
D

7
2

.

n
u
J

7
2

.
c
e
D

4
2
0
2

.

n
u
J

4
2
0
2

.

n
u
J

4
2

.
c
e
D

5
2

.

n
u
J

5
2

.
c
e
D

6
2

.

n
u
J

6
2

.
c
e
D

7
2

.

n
u
J

7
2

.
c
e
D

6.0

5.5

5.0

4.5

4.0

3.5

3.0

Current
Past GFSR

4
2

.
c
e
D

5
2

.

n
u
J

5
2

.
c
e
D

6
2

.

n
u
J

6
2

.
c
e
D

7
2

.

n
u
J

7
2

.
c
e
D

4
2
0
2

.

n
u
J

7.0

6.5

6.0

5.5

Current
Past GFSR

4
2

.
c
e
D

5
2

.

n
u
J

5
2

.
c
e
D

6
2

.

n
u
J

6
2

.
c
e
D

7
2

.

n
u
J

7
2

.
c
e
D

4
2
0
2

.

n
u
J

12

11

10

9

8

7

Sources: Bloomberg Finance L.P.; Federal Reserve; national authorities; and IMF staﬀ calculations.

Note: Expected policy rates shown here are based on interest rate futures or swaps. Information conveyed by these markets may deviate periodically from other measures
of policy rate expectations, such as those obtained from surveys of professional forecasters. Such deviations could reflect, for instance, the time-varying influence of risk
premiums embedded in yield curves.  GFSR = Global Financial Stability Report.

in setting monetary policy, relying on exchange rate
adjustments to mitigate external headwinds. With major
advanced economies having eased monetary policy,
pressure on emerging markets could moderate in the
near term. Further ahead, however, uncertainty regard-
ing trade and geopolitical policies could make preserving
financial stability more challenging. Financial flows
may become more volatile and access to international
funding may be more difficult, especially for frontier
economies. The slowing growth outlook in China and
fragilities in its financial system are key downside risks
to the global economy, as the measured policy support
so far has yet to stabilize the housing market downturn
and restore consumer and business confidence.

This Global Financial Stability Report delves into the

financial vulnerabilities and imbalances and offers rec-
ommendations on how policymakers can address them.

Monetary and Financial Developments
Monetary Policy Is Expected to Ease Globally

With postpandemic supply chain disruptions and
commodity price pressures having largely dissipated
and labor markets coming into better balance, inflation
has continued to move toward central banks’ targets,
and most have begun to ease monetary policy. Since
the April 2024 Global Financial Stability Report, the
European Central Bank, Bank of England, the Federal
Reserve, and Riksbank have cut policy rates. Meanwhile,
the Bank of Japan raised its policy rate in July, sup-
ported by broad-based wage growth projected to support
sustainable and stable achievement of its inflation target
(Figure 1.1). That said, the pace and extent of easing
delivered by different central banks are expected to vary,
with inflation still above target in many regions.

International Monetary Fund | October 2024

3

GLOBAL FINANCIAL STABILITY REPORT: STEAdYING ThE COuRSE: uNCERTAINTY, ARTIFICIAL INTELLIGENCE, ANd FINANCIAL STABILITY

Figure 1.2. Economic Uncertainty and Market-Based Inflation Expectations

Economic uncertainty and geopolitical risk are elevated, while financial
market volatility is compressed.

Upside risks to the inflation outlook over the coming year remain
meaningful.

1. Diﬀerence of the Standardized Measures of Financial Volatility,

2. Distribution of Analysts’ Survey Forecasts: One-Year-Ahead

Economic Uncertainty, and Geopolitical Risk
(Diﬀerences in historical z-scores)

Financial volatility minus economic uncertainty
Financial volatility minus geopolitical risk

Core Inflation
(Percent; probability density)

US: Latest
2024 April GFSR
2023 October GFSR

0.75

1.35

1.95

2.55

3.15

3.75

Euro area: Latest
2024 April GFSR
2023 October GFSR

2.5

2.0

1.5

1.0

0.5

0

−0.5

−1.0

−1.5

−2.0

2013 14

15

16

17

18

19

20

21

22

23

24

0.95

1.55

2.15

2.75

3.35

3.95

Interest rate derivatives suggest that both shallow and deep adjustment cuts are possible.

3. Option-Implied US Monetary Policy Scenarios over the Next Two Years

4. Option-Implied Euro Area Monetary Policy Scenarios over the Next

(Probability in percent)

Two Years
(Probability in percent)

Below 2.25

2.25 to 3.75

3.75 to 5.25

Above 5.25

Below 1

1 to 2.5

2.5 to 3.75

Above 3.75

100
90
80
70
60
50
40
30
20
10
0
Apr.
2022

No cut or hike

No cut or hike

Shallow adjustment cut

Shallow adjustment cut

Deep adjustment cut

Deep adjustment cut

Recession cut

Recession cut

Jul.
22

Oct.
22

Jan.
23

Apr.
23

Jul.
23

Oct.
23

Jan.
24

Apr.
24

Jul.
24

Apr.
2022

Jul.
22

Oct.
22

Jan.
23

Apr.
23

Jul.
23

Oct.
23

Jan.
24

Apr.
24

Jul.
24

3

2

1

0

2

1

0

100
90
80
70
60
50
40
30
20
10
0

Sources: Bloomberg Finance L.P.; and IMF staﬀ calculations.

Note: In panel 1, “economic uncertainty” is the index of Baker, Bloom, and Davis (2016); “financial volatility” is the average of the Chicago Board Options Exchange’s
Volatility Index, High-Yield Corporate Volatility Index, and Currency Volatility Index; and “geopolitical risk” is the index of Caldara and Iacoviello (2022). All series are
z-scores (from 2012 to 2024) of 12-month-moving-average values and then diﬀerences were taken. In panel 2, distributions are constructed, using kernel densities, from
survey forecast responses submitted by economists and market participants to Bloomberg Finance L.P. Forecasts for core consumer price and personal consumption
expenditures indices are shown for the euro area and the United States, respectively. GFSR = Global Financial Stability Report.

Markets are pricing in multiple cuts in policy rates
among major central banks over the remainder of this
year and during the next (Figure 1.1, panels 1–4).
The Federal Reserve is expected to cut its policy rate
by almost 150 basis points by the end of 2025, more
than was expected at the time of the April 2024 Global
Financial Stability Report. In emerging markets, policy
paths have generally been revised downward, however,
with some central banks having paused their cutting
cycles as interest differentials with respect to advanced
economy central banks have narrowed, or raised rates
to ensure convergence of inflation to target.

4

International Monetary Fund | October 2024

Financial Market Volatility Disconnected with
Economic Uncertainty

Expectations for lower policy rates globally and inves-

tor optimism have helped compress financial market
volatility despite elevated economic policy uncertainty
and geopolitical risks. The wedge between volatility and
uncertainty (Figure 1.2, panel 1) is currently quite large,
raising the risk that volatility could surge when adverse
shocks hit to exacerbate vulnerabilities. More specifi-
cally, inflation uncertainty is still elevated somewhat, as
analysts forecast that upside risks to inflation—especially
a 2 percent or higher core inflation in the year ahead—

CHAPTER 1

STEAdYING ThE COuRSE: FINANCIAL MARkETS NAvIGATE uNCERTAINTY

remain in both the euro area and the United States
(Figure 1.2, panel 2). Reflecting the wide range of possi-
ble economic outcomes ahead, investors are increasingly
attuned to signs of economic or labor market slowdown,
with some even discussing recessionary probabilities.
As a result of the dual sets of risks, financial markets
are pricing in substantial likelihood of shallow cuts as
well as deep adjustments, especially in the United States
(Figure 1.2, panels 3 and 4).

Yield Curve Disinversion Partly Reflects Higher
Expected Debt Levels

Long-term interest rates in most advanced econ-
omies and many emerging markets have changed
little, on net, since the April 2024 Global Financial
Stability Report (Figure 1.3, panel 1). In some major
emerging market economies, however, long-term rates
have seen upward pressure from rising term premiums
(Figure 1.3, panels 2 and 3), possibly reflecting higher
uncertainty about the pace and timing of policy easing
by advanced economies and volatility in exchange rates
(see “Global Monetary Policy Synchronization Leads to
More Spillovers to Emerging Markets”).

Since the April 2024 Global Financial Stability
Report, the slope of the US yield curve—for example,
the difference between 10-year and 2-year Treasury
yields—has steepened, with the yield curve disinverting
after a historically long period of inversion (Figure 1.3,
panel 4). In general, yield curves can steepen when
short-term rates fall faster than long-term rates (that is,
during a so-called bull steepening) or when longer-term
rates rise faster than short-term ones (a so-called bear
steepening). Bull steepening episodes have historically
been associated with easing of monetary policy and
with policy rates being cut, whereas increasing term
premiums—both real risk premium and inflation risk
premium components—have typically driven bear
steepening episodes (Figure 1.3, panel 5). Since the start
of the year, the expected path of short-term rates has
declined, on net, but this has occurred alongside the
term premium moving higher, with the inflation risk
premium—reflecting the compensation investors require
for bearing risks of inflation uncertainty—notably dis-
playing continued persistence (Figure 1.3, panel 6).

These changes to the yield curve are fairly unique and
could lead to indeterminacy in investors’ asset allocation
and more volatile markets. Historically, bear steepening
episodes have been more favorable to risk assets than
bull steepening episodes (Figure 1.4, panel 1) because
investors expect strong growth momentum, supporting

corporate earnings, and spurring demand for equities.3
Past bull steepening episodes, conversely, have typi-
cally occurred when investors expected a deteriorating
economic outlook, thereby weighing on risk assets. That
said, the recent steepening in the US yield curve is some-
what unique in featuring a decline in the expected policy
rate path, as in bull steepening episodes, coupled with a
rise in term premium, as in bear steepening episodes, of a
broadly comparable magnitude (see Figure 1.3, panel 5).
These two forms of steepening will likely continue to
work in tandem: The Federal Reserve will probably con-
tinue to cut rates. At the same time, Treasury issuance,
which is projected to remain high in coming years to
fund government deficits, may spur fiscal uncertainty
and concerns about the buildup of inflationary pressures
(evidenced, in part, by persistent inflation risk premium;
see previous discussion and Figure 1.3, panel 6), in turn
exerting upward pressure on term premiums (Figure 1.4,
panel 2; see also “Quantitative Tightening Has Proceeded
in an Orderly Manner So Far”).4 Amid already-high
economic uncertainty, the two steepening types operating
in tandem may add to the murkiness of signals about the
trajectory of economy.

Quantitative Tightening Has Proceeded in an
Orderly Manner So Far

Ongoing quantitative tightening has so far unfolded
in an orderly fashion, reflecting the carefully calibrated
pace and scope of balance sheet reduction by central
banks aimed at maintaining smooth functioning of
government bond and short-term funding markets.
Group of Ten central banks have reduced their balance
sheets (Figure 1.5, panel 1) from a peak of $28 trillion
in March 2022 to $21.5 trillion. The key tail risk
that remains is that quantitative tightening may drain
bank reserves too much, causing the type of squeeze in
funding markets exemplified by the US repo market
turmoil in September 2019 (see the October 2019
Global Financial Stability Report). Currently, many
central banks are engaging in quantitative tighten-
ing simultaneously, raising the odds that this type of
risk can spill over more widely—for example, inad-
equate bank reserves in one jurisdiction may end up

3Specifically, growth momentum is expected to more than offset

negative effects of high long-term rates on corporate earnings,
thereby leading to buoyant prices.

4Furthermore, long-term yields may also be bolstered by positive
correlation between equity prices and bond yields, as can be expected
during a period of high inflation uncertainty (see Aquilina and
others 2024), rendering bonds a poor hedge for risk assets and in
turn keeping term premiums high.

International Monetary Fund | October 2024

5

GLOBAL FINANCIAL STABILITY REPORT: STEAdYING ThE COuRSE: uNCERTAINTY, ARTIFICIAL INTELLIGENCE, ANd FINANCIAL STABILITY

Figure 1.3. Recent Developments in Longer-Term Interest Rates

Long-term rates remained broadly unchanged, on net, in most major advanced economies, and have moved up in many emerging markets since the
April 2024 Global Financial Stability Report.
1. Ten-Year Government Bond Yields for

2. Ten-Year Government Bond Yields for

3. Decomposition of Changes in 10-Year Bond

Selected Advanced Economies
(Percent)

Selected Emerging Market Economies
(Percent)

Yields Since April 2024 GFSR
(Basis points)

April 2024
GFSR

GBR
DEU

USA
JPN

April 2024
GFSR

IDN
MEX

BRA
POL

5

4

3

2

1

0
Oct.
2022

May
23

Dec.
23

Jul.
24

Oct.
2022

May
23

Dec.
23

Jul.
24

14

12

10

8

6

4

Average expected short term rate
Term premium
Change in yield

r
a
e
y
-

0
1

A
S
U

r
a
e
y
-

0
1

U
E
D

r
a
e
y
-

0
1

R
B
G

r
a
e
y
-

0
1

N
P
J

r
a
e
y
-

0
1

A
R
B

r
a
e
y
-

0
1

X
E
M

r
a
e
y
-

0
1

L
O
P

r
a
e
y
-

0
1

D
N

I

Advanced economies

Emerging markets

200

150

100

50

0

−50

−100

The US yield curve has disinverted after a long
period of inversion ...

4. Evolution of US Yield Curve Slope

Since 1999
(Basis points)

... with steepening reflecting both the falling
expected policy rate path and rising term
premiums.

The inflation risk premium component of the
term premium has displayed continued
persistence.

5. Decomposition of Changes in US 10-Year
Yields During Historical Steepening
Episodes Versus During Recent Period
(Basis points)

6. Inflation Risk Premium and Real Risk

Premium Components of the US 10-Year
Bond Yield
(Percent)

Bull steepening
April 2024 GFSR

Bear steepening
2s10s

Inflation risk premium
Expected inflation
Term premium
Nominal expected short rate

Real term premium
Real expected
short rate

Inflation risk premium
Real term premium

45

30

15

0

−15

−30

Bear
steepening

Bull
steepening

Change
year-to-date

Historical average of
changes during steepening
episodes since 1999

Mar.
2020

Mar.
21

Jun.
22

Jun.
23

May
24

2.0

1.5

1.0

0.5

0

−0.5

−1.0

−1.5

−150

1999

2005

11

17

23

Sources: Bank of England; Bloomberg Finance L.P.; European Central Bank; Federal Reserve; and IMF staﬀ calculations.

Note: Decomposition of bond yields into expected short-rate and term premiums in panel 3 follows the methodology of Adrian, Crump, and Moench (2013). In panel 4, the
average reaction of the US 10-year government bond yield is calculated three months into a steepening episode since 2000. Steepening episodes are defined as in
Goldman Sachs (2023). Joint decomposition of nominal and real yields into expected inflation, real expected short-term rate, inflation risk premium, and real term
premium in panel 5 follow Abrahams and others (2016). Data labels in the figure use International Organization for Standardization (ISO) country codes. 2s10s = diﬀerence
between 10-year and 2-year Treasury yields; GFSR = Global Financial Stability Report.

6

International Monetary Fund | October 2024

300

225

150

75

0

−75

CHAPTER 1

STEAdYING ThE COuRSE: FINANCIAL MARkETS NAvIGATE uNCERTAINTY

Figure 1.4. Drivers of Steepening in the US Yield Curve

Bear steepening has favored risk assets more than bull steepening.

Net issuance of Treasuries is projected to remain elevated, possibly
pushing up term premiums.

1. Bear versus Bull Steepening Episodes for Asset Returns

2. Net Supply of Treasury Bonds Relative to GDP

60
50
40
30
20
10
0
−10
−20
−30
−40
−50

(Percent)

Bear steepening
Bull steepening
Bull steepening around GFC

(Percent, annualized average)

Total marketable
Net of QE or QT

I
T

W

s
l
a
c
i
l
c
y
C

M
E
I

C
S
M

5
2
2

i
e
k
k
i
N

e
u
l
a
V
P
&
S

0
0
5
P
&
S

0
0
0
2

l
l
e
s
s
u
R

h
t
w
o
r
G
P
&
S

d
l
o
G

Y
X
D

0
0
1
E
S
T
F

s
e
v
i
s
n
e
f
e
D

0
0
6
X
X
O
T
S
o
r
u
E

r
a
e
Y
-
0
1
S
U

r
a
e
Y
-
0
1
n
a
p
a
J

r
a
e
Y
-
0
1
n
a
m
r
e
G

2008

10

12

14

16

18

20

22

24

26

28

15

12

9

6

3

0

−3

Sources: Bloomberg Finance L.P.; and IMF staﬀ calculations.

Note: Panel 1 shows the average annualized returns for selected assets over diﬀerent steepening periods. Goldman Sachs (2023) defines the steepening episodes. Panel 2
shows net duration supply, expressed in terms of 10-year equivalent bonds net of domestic central bank purchases. Forecasts reflect consensus expectations for bond
issuance and domestic Federal Reserve purchases. Higher values indicate deteriorating liquidity. DXY = US Dollar Index; FTSE = Financial Times Stock Index; GFC = global
financial crisis; QE = quantitative easing; QT = quantitative tightening; WTI = West Texas Intermediate (crude oil).

Figure 1.5. Shifts in Government Bond Buyer Base Toward Price-Sensitive Investors amid Progress on Quantitative
Tightening

Since the April GFSR, advanced economy central banks continued
reducing their footprint in domestic sovereign bond markets.

ECB’s QT is shifting the EGB buyer base toward price-sensitive investors,
with scarcity premium arising as banks are favoring core sovereign bonds.

1. Central Bank Holdings as Share of Domestic Sovereign Bond Markets

2. Flows into Core and Noncore EGBs by Investor Type

(Percent)

(Percentage points)

60

50

40

30

20

10

0

7
1
0
2

Japan

Euro
area

Sweden

United
Kingdom

United
States

Canada

Australia

New
Zealand

8
1

9
1

0
2

1
2

2
2

3
2

4
2

8
1

9
1

0
2

1
2

2
2

3
2

4
2

7
1
0
2

60

50

40

30

20

10

0

ECB
Foreign

Domestic banks
Investment funds

Other domestic
Pensions and insurers

Core

Jun. 2023

Sep. 23

Dec. 23

Mar. 24

Jun. 24

Noncore

Jun. 2023

Sep. 23

Dec. 23

Mar. 24

Jun. 24

4

2

0

−2

−4

4

2

0

−2

−4

Sources: Bank of England; Bank of Japan; Bloomberg Finance L.P.; EUROPACE AG/Haver Analytics; European Central Bank; Federal Reserve System; national debt
management oﬃces; Reserve Bank of Australia; Reserve Bank of Canada; Reserve Bank of New Zealand; Swedish Riksbank; and IMF staﬀ calculations.

Note: Panel 1 features eight central banks representative of the major bond holdings across the Group of Ten countries. Due to data limitations, panel 1 uses relative bond
holdings (ignoring maturity profiles) to allow for cross-country comparison. Panel 2 shows cumulative flows using changes in nominal bond holdings relative to
outstanding issued amounts of core and noncore EGBs using the ECB’s Securities Holdings Statistics by Sector and Securities Issues Statistics databases. Domestic
investors reflect aggregate debt holders within the euro area. ECB = European Central Bank; EGB = European government bond; GFSR = Global Financial Stability Report;
QT = quantitative tightening.

International Monetary Fund | October 2024

7

GLOBAL FINANCIAL STABILITY REPORT: STEAdYING ThE COuRSE: uNCERTAINTY, ARTIFICIAL INTELLIGENCE, ANd FINANCIAL STABILITY

Figure 1.6. Expansion of Primary Dealers’ Treasury Holdings amid Rising Free-Floating Securities and Share of Bills

The share of bills in the market has risen amid more free-floating Treasury
securities.

Primary dealers are increasingly warehousing longer-dated securities.

1. Bills Share and Treasury Free Float Since the Mid-1970s

2. Dealer Inventory and Hedge Fund Holdings as a Share of Treasury

(Percent)

Steady and predictable

15–20% recommended bills

100

Debt Outstanding
(Percent)

Primary dealers %
Hedge funds %

80

60

40

20

0

Bill share net of Federal Reserve
Treasury free float (right scale)

Jan.
81

Jul.
85

Jan.
90

Jul.
94

Jan.
99

Jul.
2003

Jan.
08

Jul.
12

Jan.
17

Jul.
21

Aug.
2015

Aug.
16

Aug.
17

Aug.
18

Aug.
19

Aug.
20

Aug.
21

Aug.
22

Aug.
23

Aug.
24

14

12

10

8

6

4

2

0

−2

50

40

30

20

10

0
Jul.
1976

Sources: Bloomberg Finance L.P.; EUROPACE AG/Haver Analytics; Federal Reserve Bank of New York; Kuttner (2006); and IMF staﬀ calculations.

Note: In panel 2, “Hedge funds” reflects the percentage share of Treasury securities held by households and nonprofit organizations—by and large composed of hedge
funds—relative to outstanding marketable Treasury securities. “Primary dealer” reflects the percentage share of primary dealer positions in Treasury coupon securities
relative to the corresponding outstanding issued amount.

amplifying funding pressures in others because globally
active banks rely on interconnected funding markets.
Continued vigilance on the part of central banks to
monitor for possible strains in funding markets is
needed to preemptively mitigate this tail risk.

Another risk is that quantitative tightening could
increase bouts of volatility in government bond markets.
As central banks reduce their holdings of government
bonds, regardless of whether they are doing so using active
or passive5 methods, the buyer base of these bonds could
continue to move toward more price-sensitive investors.
In the euro area, the European Central Bank’s reduced
holdings of bonds of core issuers like Germany have been
offset by more holdings by domestic banks and foreign
investors (Figure 1.5, panel 2), who value German bonds
for liquidity management and regulatory capital pur-
poses. By contrast, for bonds of noncore issuers, reduced
European Central Bank holdings are offset by “other
domestic investors,” which include households and the
more price-sensitive hedge fund sector.6 This trend would

5Some—including the Bank of England, the Reserve Bank of New

Zealand, and Riksbank—are taking an active approach by selling
bonds alongside maturing assets, while others—including the Federal
Reserve and European Central Bank—are taking a passive approach,
by allowing bonds to roll off without reinvesting.

6For comparability with US flow of funds data, other domestic

investors as holders of European Government Bonds include as
categories households and domestic hedge funds, among others. For
the increasing role of the latter, see also “Hedge Funds: Good or
Bad for Market Functioning?” European Central Bank, blog post,
September 23, 2024.

8

International Monetary Fund | October 2024

introduce more volatility in noncore bond markets;
should government bond issuance increase—for example,
to finance persistent fiscal deficits—higher volatility could
be further amplified.

In the United States, quantitative tightening has
increased the share of free float Treasury securities, or
the portion of outstanding securities net of the Fed-
eral Reserve’s holdings, which could exert an upward
push on Treasury yields and volatility over time.
Concurrently, the Department of the Treasury has
increasingly issued more shorter-term debt to meet
funding needs, which might lower borrowing costs
in the near term, but could also expose the Treasury
to higher future financing cost (Figure 1.6, panel 1).7
As larger issuances have increased the prudence of
other Treasury security buyers, only hedge funds and
dealers have kept more securities on balance sheet

7Tentatively speaking, an economic backdrop of uneven nor-
malization of inflation and potential economic deceleration amid
unprecedented fiscal supply creates push and pull factors that induce
higher volatility in Treasury yields. Some market commentators
have linked this backdrop to a potential bear steepening (or bear
twist), as investors increasingly favor intermediate maturities with
Federal Reserve rate cuts coming into better focus. A historical
outperformance of these securities over bills during previous easing
cycles underscores this trend. Additionally, price-sensitive market
participants remain apprehensive regarding longer-dated Treasury
securities, whose real term premiums are being perceived amid the
elevated fiscal supply as insufficient to offset the risks of interest rate
changes during the life of the bond, as outlined in the April 2024
Global Financial Stability Report.

CHAPTER 1

STEAdYING ThE COuRSE: FINANCIAL MARkETS NAvIGATE uNCERTAINTY

Figure 1.7. Global Asset Prices

An equity rally was interrupted in August by economic slowdown
concerns ...

... as implied volatility for equities spiked.

1. Equity Performance in Advanced and Emerging Market Economies

2. Equity Market Implied Volatility in Japan and the United States

(Price index, January 1, 2023 = 100)

(Percent)

170

160

150

140

130

120

110

100

90

Jan.
2023

April 2024
GFSR

August 5

VIX Index
Nikkei Volatility Index

April 2024
GFSR

August 5

Japan
World
EM
EM ex China
US
AE

Apr.
23

Jul.
 23

Oct.
23

Jan.
24

Apr.
24

Jul.
24

Jan.
2023

Apr.
23

Jul.
23

Oct.
23

Jan.
24

Apr.
24

Jul.
24

Investment-grade corporate spreads were narrowing until August ...

... as were high-yield spreads.

3. Investment-Grade Corporate Bond Spreads

(Basis points)

4. High-Yield Corporate Bond Spreads

(Basis points)

225

205

185

165

145

125

105

85

Jan.
2023

US

Europe

UK

April 2024
GFSR

August 5

US

Europe

UK

April 2024
GFSR

August 5

Apr.
23

Jul.
23

Oct.
23

Jan.
24

Apr.
24

Jul.
24

Jan.
2023

Apr.
23

Jul.
23

Oct.
23

Jan.
24

Apr.
24

Jul.
 24

80

70

60

50

40

30

20

10

700

650

600

550

500

450

400

350

300

Sources: Bloomberg Finance L.P.; and IMF staﬀ calculations.

Note: Panel 1 uses the S&P 500 for the United States, Nikkei 225 for Japan, and corresponding MSCI indices for all other series. In panel 2, the Chicago Board Options
Exchange Volatility Index, or VIX, is the benchmark measure of US stock market volatility, based on S&P 500 options. Its Japanese counterpart is the Nikkei Stock Average
Volatility Index. Panels 3 and 4 employ option-adjusted spreads. Gray vertical lines in panels 1 through 4 mark the date of the early August sell-oﬀ peak (August 5). AE =
advanced economies; EM = emerging markets; ex = excluding; GFSR = Global Financial Stability Report.

(Figure 1.6, panel 2).8,9 Bloated dealer inventory pres-
ents a medium-term risk because in adverse market
conditions where investors are selling Treasury securi-
ties (for example, if hedge funds were to unwind the
Treasury basis trades as described in the April 2024
Global Financial Stability Report) primary dealers with
larger Treasury inventories are more likely to face

8Unlike intermediaries such as brokers or primary dealers who
might buy these securities to facilitate trading and liquidity in the
market, end users, including pension funds, insurance companies,
mutual funds, corporations, and individual investors, among others,
are typically the ultimate holders of Treasury securities.

9Since the April 2024 Global Financial Stability Report, the rise in
household Treasury holdings (primarily driven by hedge funds) has
slowed, consistent with the increased warehousing by primary dealers
shown in the latest Federal Reserve Board flow of funds statistics.

internal balance sheet constraints that could prevent
them from absorbing the sales, worsening the sell-off.

Lofty Risk Assets Valuations Is a Vulnerability

The rally in global equity markets fueled by expec-
tations of a global soft landing has continued since the
April 2024 Global Financial Stability Report, although
it was briefly interrupted by a severe but transitory
sell-off in early August (Figure 1.7, panel 1). But even
after accounting for solid economic and earnings
outlook, equity valuations appear stretched in various
parts of the world, which is a vulnerability to financial
stability. Since April, Canada, China, and the United
States have experienced the largest equity gains, with

International Monetary Fund | October 2024

9

GLOBAL FINANCIAL STABILITY REPORT: STEAdYING ThE COuRSE: uNCERTAINTY, ARTIFICIAL INTELLIGENCE, ANd FINANCIAL STABILITY

performance in the latter predicated on an impressive
run among information technology stocks. Signs of
moderating inflation in early July prompted investors
to rotate holdings into more rate-sensitive stocks,
leading information technology stocks to underper-
form and small cap stocks to outperform notably.
Then in late July and early August, the policy rate
increase by the Bank of Japan was followed by worse-
than-expected labor market data in the United States
that renewed recession fears. These developments led
to a brisk narrowing of the interest rate differential
between Japan and the United States. This boosted
the yen, which in turn reportedly led to a substan-
tial unwinding of carry trades that used the yen as a
funding currency to finance long positions in global
stocks or emerging market currencies. An abrupt
decline in stock prices around the world, along with
a spike in volatility (Figure 1.7, panel 2; see also Box
1.3), ensued. Corporate bond spreads also widened
for investment-grade and high-yield issuers in Europe
and the United States after a long period of decom-
pression (Figure 1.7, panels 3 and 4). NBFIs like
momentum-following and commodity trading advisor
hedge funds and algorithmic and quantitative traders
reportedly contributed to the sell-off, as their strate-
gies stipulated cutting of positions to stop losses (see
“Hedge Funds Were Both Catalysts and Victims of the
August Market Sell-Off ”).

Before the sell-off, positive earnings momentum
and expectations of lower interest rates had pushed
up stock prices since the April 2024 Global Financial
Stability Report and equity risk premium has increased
somewhat (see Figure 1.8, panel 1). But stock valua-
tions are still lofty, risking abrupt corrections. Since
January, the share of the Magnificent 7 (M7),10 a
group of large capitalization technology stocks, has
increased from 20 to 30 percent of the overall S&P
500 index (market capitalization). Alongside evidence
of an increase in correlation between the M7 and
S&P (and within the M7) over recent months,11 this
would suggest that the overall index is more vulnerable
to adverse developments among this group—that is,
raising the level of concentration risk. In this regard,

10The Magnificent 7 companies are Alphabet (Google), Amazon,

Apple, Meta Platforms, Microsoft, Nvidia, and Tesla.

11Correlation estimates are calculated as the rolling six-month
correlation of daily returns between the average M7 stock and S&P
500. Estimates indicate the correlation between M7 and the S&P
has increased from around 40 percent to just above 65 percent since
May. Correlation of average pairwise M7 has increased from 10 to
50 percent over the same period.

10

International Monetary Fund | October 2024

since 2023, there have been 69 days on which fewer
than 150 stocks have moved in the same direction than
the index (Figure 1.8, panel 2), signaling that headline
index returns do not represent the performance of
the majority of the constituents, as fewer stocks have
dictated index movements.

More fundamentally, the S&P 500 is trading at a

level above its historical upper quartile in terms of
forward price-to-earnings ratio since 1990, suggesting
that the market is expecting high earnings growth over
the near to medium term. For this ratio to return to its
historical 10-year average by 2026, earnings per share
on of the S&P and Nasdaq would need to post com-
pounded annual growth rates of close to 25 and 30
percent, respectively, which are far higher than current
market expectations (Figure 1.8, panel 3); the MSCI
World and MSCI Advanced Economy indices all
require higher growth rates than current expectations
to return to historical valuations, a sign that prices are
lofty. By contrast, emerging market indices and the
Russell 2000, an index of small capitalization stocks
in the United States, are experiencing less pressure on
current valuations, as required earnings growth to meet
historical valuations is less than current expectations.
Declining inflation and expectations of US mone-
tary policy easing led to a significant rotation across
indices, with the Russell 2000 outperforming the
Nasdaq by about 10 percentage points between the
beginning of July and early August, as investors appear
to shift from growth toward smaller stocks with
less-demanding valuation (Figure 1.8, panel 4). How-
ever, as the equity market correction in early August
showed, concerns about a slowing of the real economy
disproportionally affect smaller stocks.

Market turbulence in early August has so far not
affected emerging market assets significantly. Sovereign
spreads for emerging market bonds denominated in
US dollars have remained tight relative to spreads on
investment-grade corporate bonds since the April 2024
Global Financial Stability Report (Figure 1.9, panel 1),
and spreads between local currency bonds and some
Latin American sovereigns have widened, with upward
revisions to policy rate paths partly driving the move-
ment (Figure 1.9, panel 2). The performance of emerg-
ing market equities has varied across countries this
year, but for most countries, valuations remain below
historical averages (Figure 1.9, panel 3). Various factors
may challenge emerging market assets in the months
ahead, including uncertainty induced by monetary
policy in advanced economies—especially the United

CHAPTER 1

STEAdYING ThE COuRSE: FINANCIAL MARkETS NAvIGATE uNCERTAINTY

Figure 1.8. Concentration, Expectations Regarding Growth, and Rotation in the Current Equity Rally

Stocks rallied on positive earnings momentum and supportive risk
premiums.

The fewest number of stocks are moving in the same direction as the
index since 1997, with M7 stocks dictating index movements recently.

1. S&P 500 Decomposition

(Price index, January 1, 2023 = 100)

2. S&P 500 Daily Returns and Constituent Movements

(Daily returns, percent; number of constituents)

70

60

50

40

30

20

10

0

−10

−20

Earnings
ERP

Rates
Price returns

1997–2023
2023–24

3
2

.

b
e
F

3
2

.
r
a
M

3
2

.
r
p
A

3
2

.
y
a
M

3
2

.

n
u
J

3
2

.

g
u
A

3
2

.

p
e
S

3
2

.
t
c
O

3
2

.
v
o
N

3
2

.
c
e
D

4
2

.

n
a
J

4
2

.
r
a
M

4
2

.
r
p
A

4
2

.
y
a
M

4
2

.

n
u
J

4
2

.
l

u
J

4
2

.

g
u
A

4
2

.

p
e
S

3
2
0
2

.

n
a
J

−5

−4

−3

−2

−1

0

1

2

3

4

500

400

300

200

100

0

5

Current valuations of technology stocks demand high earnings growth,
while smaller stocks appear undervalued.

Expectations of declining inflation and falling rates led to strong asset
rotation across indices.

3. Required Earnings Growth to Justify Existing Valuation

4. Return Ratios and Volatility of Large Stocks Relative to That of

75

50

25

0

(Percent)

Growth 2024–26 CAGR
Required growth to return to historical multiple

World

AEs

EMs

EMs ex
China

Japan

US

Nasdaq Russell
2000

Small Stocks
(Ratios)

1.65

1.55

1.45

1.35

1.25

1.15

1.05

0.95

Jan.
2023

Large to small cap volatility (right scale)
SPX/SPW
Nasdaq/Russell 2000
Growth/value

June CPI
release

Apr.
23

Jul.
23

Oct.
23

Jan.
24

Apr.
24

Jul.
24

1.00

0.90

0.80

0.70

0.60

0.50

Sources: Bloomberg Finance L.P.; EUROPACE AG/Haver Analytics; Thomson Reuters IBES and DataStream; and IMF staﬀ calculations.

Note: In panel 3, “required growth to return to historical multiple” is calculated as the CAGR required to make the three-year-forward (end of 2026) price-to-earnings ratio
to return to its 10-year historical average. Dashed vertical line indicates indices within the United States. In panel 4, ratios are based on weekly returns. “Large to small cap
volatility” is the implied volatility for the S&P 500 divided by the implied volatility for the Russell 2000. AEs = advanced economies; CAGR = compound annual growth
rate; CPI = consumer price index; EMs = emerging markets; ERP = equity risk premium; ex = excluding; M7 = Magnificent 7; SPW = S&P 500 Equal Weighted Index;
SPX = S&P 500 Index.

States—and the policies of newly elected governments
around the world, especially those that would affect
the geopolitical landscape and fragmentation risks.

The crypto rally earlier this year has started to fade,

as optimism spurred by the approval of spot Bitcoin
and Ethereum exchange-traded products in January
and May 2024, respectively, appears to have dissi-
pated (Figure 1.10, panel 1). Meanwhile, the total
market capitalization of crypto assets at $2.2 trillion
remains below its historical peak in November 2021.
Crypto valuations have been driven recently by high

rolling correlation between Bitcoin and other asset
classes, such as equities (S&P 500) and gold, rather
than idiosyncratic developments within this asset
class (Figure 1.10). Widespread adoption of crypto
assets could undermine the effectiveness of monetary
policy, circumvent measures for managing capital flows
(Cerutti, Chen, and Hengge 2024), exacerbate fiscal
risks, divert resources available for financing the real
economy, and threaten global financial stability. In
addition, the growing interlinkages between crypto
and broader financial markets, including the increasing

International Monetary Fund | October 2024

11

GLOBAL FINANCIAL STABILITY REPORT: STEAdYING ThE COuRSE: uNCERTAINTY, ARTIFICIAL INTELLIGENCE, ANd FINANCIAL STABILITY

Figure 1.9. Performance of Assets in Emerging Markets

US dollar emerging market sovereign spreads
remain tight relative to US investment-grade
firms.

Emerging market government local yields
remained broadly stable.

1. Spread of Emerging Market Sovereign

2. Spreads of Local 10-Year Government

External Debt Against US Investment-Grade
Firms, Spreads of US Investment-Grade
Firms Against Treasuries
(Basis points)

LATAM
CEEMEA
Asia
US IG corp.

LATAM, 2015–19
CEEMEA, 2015–19
Asia, 2015–19

Bonds as a Share of 10-Year Treasury Yields,
and 10-Year Treasury Yields
(Percent)

LATAM
CEEMEA
Asia
UST 10 yr

LATAM, 2015–19
CEEMEA, 2015–19
Asia, 2015–19

Emerging market equities have performed
positively this year, though valuations remain
lower than historical averages.

3. Year-to-Date Total Returns to Equity in
Emerging Markets and Normalized
Forward Price-to-Earnings Ratio Against
Historical 10 Years
(Percent of amounts in local currency; z-score)

LATAM

CEEMEA

Asia

200

150

100

50

0

−50

−100

1
2

1
2

1
2

2
2

2
2

2
2

2
2

3
2

3
2

3
2

3
2

4
2

4
2

4
2

.
r
p
A

.
l

u
J

.
t
c
O

.

n
a
J

.
r
p
A

.
l

u
J

.
t
c
O

.

n
a
J

.
r
p
A

.
l

u
J

.
t
c
O

.

n
a
J

.
r
p
A

.
l

u
J

1
2
0
2

.

n
a
J

1
2
0
2

.

n
a
J

1
2

1
2

1
2

2
2

2
2

2
2

2
2

3
2

3
2

3
2

3
2

4
2

4
2

4
2

.
r
p
A

.
l

u
J

.
t
c
O

.

n
a
J

.
r
p
A

.
l

u
J

.
t
c
O

.

n
a
J

.
r
p
A

.
l

u
J

.
t
c
O

.

n
a
J

.
r
p
A

.
l

u
J

8
7
6
5
4
3
2
1
0

3

2

1

0

−1

−2

)
e
r
o
c
s
-
z
d
e
z
i
l
a
m
r
o
n
(
E
/
P
d
r
a
w

r
o
F

−30

30
2024 YTD Returns (local currency, percent)

−10

10

Sources: Bloomberg Finance L.P.; JPMorgan; MSCI; and IMF staﬀ calculations.

Note: Fourteen major emerging markets are included in the calculations. Asia = India, Indonesia, Malaysia, the Philippines, Thailand; CEEMEA = Hungary, Poland,
Romania, South Africa; LATAM = Brazil, Chile, Colombia, Mexico, Peru. Thailand is excluded from panel 1 because the sovereign has no outstanding hard-currency
dollar-denominated debt. For panel 3, the z-score is calculated from the distribution of monthly observations of forward price-to-earnings ratios of the respective MSCI
equity indices from January 2014 to August 2024. CEEMEA = Central and Eastern Europe, the Middle East, and Africa; corp. = corporations; IG = investment grade;
LATAM = Latin America; P/E = price to earnings; UST 10 yr = Treasury 10-year yield; YTD = year to date.

Figure 1.10. Fading Rally in Crypto Assets

Optimism with regard to crypto assets has dissipated over the course of
2024 so far.

High correlations between Bitcoin and other asset classes suggest that
broader risk sentiment drives crypto markets.

1. Prices of Selected Crypto Assets and Approvals of Products Traded on

2. Correlation Between Bitcoin and Other Assets

Spot Exchanges
(Indexed, January 1, 2024 = 100)

(One-year rolling correlation)

Bitcoin

Ethereum

Solana

Binance coin

Ethereum

S&P 500

Gold

UST 10-year yield

240

220

200

180

160

140

120

100

80

Jan.
2024

BTC ETP approval

ETH ETP approval

Feb.
24

Mar.
24

Apr.
24

May
24

Jun.
24

Jul.
24

Aug.
24

Sep.
24

0
2

.
r
p
A

0
2

.
l

u
J

0
2

.
t
c
O

1
2

.

n
a
J

1
2

.
r
p
A

1
2

.
l

u
J

1
2

.
t
c
O

2
2

.

n
a
J

2
2

.
r
p
A

2
2

.
l

u
J

2
2

.
t
c
O

3
2

.

n
a
J

3
2

.
r
p
A

3
2

.
l

u
J

3
2

.
t
c
O

4
2

.

n
a
J

4
2

.
r
p
A

4
2

.
l

u
J

0
2
0
2

.

n
a
J

1.5

1.0

0.5

0

−0.5

−1.0

Sources: Bloomberg Finance L.P.; CoinGecko; and IMF staﬀ calculations.

Note: Panel 2 shows the one-year rolling correlation between Bitcoin prices and the prices (or yields) of the assets. BTC = Bitcoin; ETH = Ethereum; ETP = exchange-trade
product; UST = US Treasuries.

12

International Monetary Fund | October 2024

CHAPTER 1

STEAdYING ThE COuRSE: FINANCIAL MARkETS NAvIGATE uNCERTAINTY

Figure 1.11. Financial Conditions Indices

Financial conditions have remained accommodative in advanced
economies ...

... driven, in part, by improved corporate valuations.

1. Financial Conditions Indices

(Number of standard deviations over the long-term average)

2. Key Drivers of Financial Conditions Indices

(Number of standard deviations over the long-term average)

1.5

1.0

0.5

0

−0.5

−1.0

−1.5

Mar.
2021

United States
Other advanced economies
EMs ex-China

Euro area
China

Interest rates
External financing risk

Corporate valuations
Aggregate

House prices

April
2024
GFSR

United
States

Euro area

Other AEs

China

EMs
ex-China

Sep.
21

Mar.
22

Sep.
22

Mar.
23

Sep.
23

Mar.
24

Sep.
24

4
Q
:
3
2

1
Q
:
4
2

2
Q
:
4
2

3
Q
:
4
2

3
Q
:
3
2
0
2

4
Q
:
3
2

1
Q
:
4
2

2
Q
:
4
2

3
Q
:
4
2

3
Q
:
3
2
0
2

4
Q
:
3
2

1
Q
:
4
2

2
Q
:
4
2

3
Q
:
4
2

3
Q
:
3
2
0
2

4
Q
:
3
2

1
Q
:
4
2

2
Q
:
4
2

3
Q
:
4
2

3
Q
:
3
2
0
2

4
Q
:
3
2

1
Q
:
4
2

2
Q
:
4
2

3
Q
:
4
2

3
Q
:
3
2
0
2

0.6

0.4

0.2

0

−0.2

−0.4

−0.6

−0.8

−1.0

Sources: Bloomberg Finance L.P.; Dealogic; EUROPACE AG/Haver Analytics; national data sources; and IMF staﬀ calculations.

Note: The IMF’s Financial Conditions Index is designed to capture the pricing of risk. It incorporates various pricing indicators, including real house prices, but does not
include balance sheet or credit growth metrics. For details, see Online Annex 1.1 in the October 2018 GFSR. Panel 2 shows the key drivers of financial conditions indices in
terms of the contribution of underlying components, which is the weighted average of the z-scores of these components. “Aggregate” represents the sum of these
contributions, and its values are similar, but not identical, to Financial Conditions Index values shown in panel 1. AEs = advanced economies; EMs ex-China = emerging
markets excluding China; GFSR = Global Financial Stability Report.

involvement of incumbent providers of financial
services, may increase contagion risks in the future (see
Box 1.2).

easy on net, external financing costs have risen, offset-
ting the impact of corporate valuations.

Growth-at-Risk, the Global
Macrofinancial Stability Assessment
Financial Conditions Are Still
Accommodative Globally

Financial conditions have marginally tightened in
many regions, having been somewhat affected by the
market turmoil in early August (Figure 1.11, panel 1).
Still-elevated equity and corporate bond valuations
have kept financial conditions in advanced econo-
mies relatively easy by historical standards. In China,
where growth outlook and property sector issues had
been weighing down risk sentiment over the past year,
financial conditions measured by price indicators have
loosened as a result of monetary policy easing, a nar-
rowing in corporate credit spreads, and some dimin-
ishment of external headwinds (Figure 1.11, panel 2),
while quantity indicators such as credit growth keep
weakening. Although overall financial conditions in
other emerging market economies have been slightly

Risks to Financial Stability Moderate in the Near
Term; More Elevated over the Medium Term

The updated GaR assessment indicates that over the

next year, there is a 5 percent probability that global
real growth will fall below 1.2 percent (Figure 1.12,
panel 1, black distribution and marker).12 Although
this is appreciably lower than the baseline forecast for
growth of 3.2 percent in the World Economic Outlook,
GaR is around the 40th historical percentile, indicating
that near-term risk is contained owing to still accom-
modative financial conditions and moderate credit
growth (Figure 1.12, panel 2). The forecast distribution
of growth is skewed slightly more to the left than the
forecast in the April 2024 Global Financial Stability
Report, in line with the World Economic Outlook’s
assessment that balance of risk to the global outlook is
tilted to the downside. However, if financial conditions

12The GaR framework assesses downside risks by gauging the
range of severely adverse growth outcomes falling within the lower
5th percentile of the conditional growth forecast distribution (that is,
the GaR metric).

International Monetary Fund | October 2024

13

GLOBAL FINANCIAL STABILITY REPORT: STEAdYING ThE COuRSE: uNCERTAINTY, ARTIFICIAL INTELLIGENCE, ANd FINANCIAL STABILITY

Figure 1.12. Global Growth-at-Risk

Downside risk to global growth remain elevated over the medium term.

1. Growth Forecast Densities

(Probability densities)

2. Near-Term Growth-at-Risk Forecasts

3. Medium-Term Growth-at-Risk Forecast

(Historical percentile rank)

(Historical percentile rank)

0.50
0.45
0.40
0.35
0.30
0.25
0.20
0.15
0.10
0.05
0
−3

Density for
year 2024:
at 2024:Q1

Density for
year 2025:
at 2024:Q3

Scenario

5th percentile

0

3

6

2012 13 14 15 16 17 18 19 20 21 22 23 24

0

20

40

60

80

100

0

20

40

60

80

100

2012 13 14 15 16 17 18 19 20 21 22 23 24

Sources: Bank for International Settlements; Bloomberg Finance L.P.; EUROPACE AG/Haver Analytics; IMF, International Financial Statistics database; and IMF staff
calculations.
Note: In panel 1, the mode (that is, the most likely outcome) of the estimate for current forecast density accords with the IMF, World Economic Outlook forecast for global
growth for 2025, as of 2024:Q3. In panels 2 and 3, the black line traces the evolution of the 5th percentile threshold (the growth-at-risk metric) of the near-term and
medium-term growth forecast densities. The quintile with the lowest percentile rank is bright red and with the highest bright green. The intensity of the shading depicts
the percentile rank for the growth-at-risk metric. The scenario used in panel 1 calibrates the response of global financial conditions to a spike in Chicago Board Options
Exchange Volatility Index, as seen on August 5, 2024 (EDT). The level of Chicago Board Options Exchange Volatility Index used to calibrate the FCI tightening is computed
as the average of intraday VIX, recorded at five-minute intervals, from the start of business on August 5 to end of business on that day. Response functions of financial
conditions are computed via linear regression methods applied separately over the full sample and the post–COVID-19 sample. The full sample regression starts in 1991:Q1,
whereas the post–COVID-19 sample estimation begins in 2021:Q1, both running until the current period. A 75 percent weight is applied to the post–COVID-19 regression
estimates to match comparable co-movements in Chicago Board Options Exchange Volatility Index and financial conditions during several historical stress episodes.

were to tighten by 2.5 standard deviations—broadly
corresponding to the average of the intraday increases of
the Chicago Board Options Exchange Volatility Index
level on August 5 relative to its level at the open—and
remain at that restrictive level for one quarter, the year-
ahead GaR could worsen to its lowest historical quintile
(see Figure 1.12, panel 1, dotted green distribution and
green marker; and panel 2). This demonstrates that an
abrupt tightening in financial conditions could raise
near-term financial stability risks.

On the other hand, downside risk over the medium
term, as indicated by GaR four years ahead, has been
at around historically elevated levels since 2023. And
while it has improved some over the past year, it
remains at its worst quintile currently (Figure 1.12,
panel 3). Easy financial conditions and strong credit
growth have an intertemporal trade-off: Although they
reduce near-term risks, they also prompt a buildup
of vulnerabilities—like increased debt and leverage
documented across this report—that raises downside
risks in coming years. This intertemporal trade-off is
more acute when economic uncertainty is elevated like
at present, and importantly, the trade-off is nonlinear
in uncertainty because it also depends on the size of
the disconnection between economic uncertainty and
market volatility (see Chapter 2).

14

International Monetary Fund | October 2024

Emerging Markets
Emerging Market Resilience Challenged by
Uncertainty

Emerging markets have confronted a multitude
of global shocks and elevated economic uncertainty
since the pandemic, deploying proactive monetary
policy and in certain cases measures related to foreign
exchange to strengthen their resilience to external
headwinds (Adrian, Natalucci, and Wu 2024). As a
result, the aggregate heat map for emerging market
assets shows that market stress has remained largely
moderate in interest rates, foreign exchange, and
other assets (Figure 1.13). The market turmoil in
advanced economies in early August has not changed
this assessment. Looking ahead, as advanced economy
central banks cut interest rates while global growth
remains resilient, the dollar could weaken and investor
sentiment on emerging market assets could turn more
positive, spurring renewed portfolio inflows.

However, global uncertainty would likely remain
elevated owing to geopolitical developments as well as
uncertain future policies of newly elected governments.
Some countries will likely have to navigate further exter-
nal headwinds while coping with some idiosyncratic
risks that led the recent depreciation of some emerging

CHAPTER 1

STEAdYING ThE COuRSE: FINANCIAL MARkETS NAvIGATE uNCERTAINTY

Figure 1.13. Continuing Resilience in Emerging Markets

Emerging markets continue to exhibit broad resilience, although some markets are showing evidence of pressures.
Financial Market Stress in Emerging Markets

Low

Market stress

High

Short-Term Policy Rates
  Ex ante real policy rate
  Ex post real policy rate
FX Market (vs. USD)
  ATM-option-implied vol.
  FX-implied yield diﬀ.
External Funding Market
  Sovereign five-year CDS
  EM sovereign spread (vs. UST)
Domestic Bonds
  Govt. 2s10s
  Local corporate spread
Domestic Equities
  Equity realized vol.
  Overall forward P/E ratio

Sep.
2014

Mar.
15

Sep.
15

Mar.
16

Sep.
16

Mar.
17

Sep.
17

Mar.
18

Sep.
18

Mar.
19

Sep.
19

Mar.
20

Sep.
20

Mar.
21

Sep.
21

Mar.
22

Sep.
22

Mar.
23

Sep.
23

Mar.
24

Sep.
24

Sources: Bloomberg Finance L.P.; EUROPACE AG/Haver Analytics; JPMorgan; MSCI; and IMF staﬀ calculations.

Note: The heat map reflects funding situations based on key financial market indicators across 14 major emerging markets, including Brazil, Chile, Colombia, Hungary,
India, Indonesia, Malaysia, Mexico, the Philippines, Thailand, Peru, Poland, Romania, and South Africa. Each country’s indicators are transformed into a normalized z-score
based on 10 years of monthly observations. High market stress (darkest red) reflects observations exceeding two standard deviations from mean (z-score > +2), and low
market stress (darkest green) reflects observations more than two standard deviations below mean (z-score < −2). 2s10s = 2-year and 10-year local currency government
yield diﬀerentials; ATM = at-the-money; CDS = credit default swaps; EM = emerging market; FX = foreign exchange; P/E = price-to-earnings; USD = US dollar; UST = US
Treasuries; vol = volatility.

market currencies. Therefore, divergence among the
emerging markets universe may be more pronounced
down the road. Financial conditions for frontier mar-
kets remain challenging, with many countries that are
grappling with higher borrowing costs and financial
instability still not having access to funding through
international markets despite sovereign spreads that are
moderating lower.

Global Monetary Policy Synchronization Leads to
More Spillovers to Emerging Markets

Positive interest rate differentials in emerging

markets vis-à-vis advanced economies, a key source of
resilience in 2023, have generally narrowed since the
April 2024 Global Financial Stability Report, which
has put some pressure on emerging market currencies
(Figure 1.14, panel 1). At the same time, increased
volatility in financial markets, including the rapid
appreciation in early August in the Japanese yen—a
common funding currency—have made carry trades
less attractive on a risk-adjusted basis (Figure 1.14,
panel 2; see also Box 1.3). But the narrowing of
interest rate differentials and less attractive carry do not

fully explain the year-to-date depreciation in emerging
market currencies. Indeed, an IMF staff model finds
that whereas the carry factor was the dominant driver
of currency moves in 2023, an idiosyncratic factor,
a proxy for domestic policy risks and uncertainty in
global markets, has played an important role in 2024
alongside the strength of the US dollar, notably for
Latin American currencies and the South African rand
(Figure 1.14, panel 3). High-yield sovereigns and com-
modity exporters have generally been more susceptible
to larger swings in foreign exchange rates. To manage
the consequences of such an external shock, several
central banks in emerging markets had turned more
cautious and slowed or paused their rate cut cycles.
Some central banks in emerging markets have also
conducted foreign exchange interventions to smooth
currency volatility. The Fed rate cut in September and
the subsequent weakening of the US dollar have eased
some of the pressures faced by EM central banks, and
markets continue to expect easing across emerging
markets broadly.

Henceforth, after two years of decoupling, market
participants expect monetary policy cycles in emerging
markets to be more synchronized with those in the

International Monetary Fund | October 2024

15

GLOBAL FINANCIAL STABILITY REPORT: STEAdYING ThE COuRSE: uNCERTAINTY, ARTIFICIAL INTELLIGENCE, ANd FINANCIAL STABILITY

Figure 1.14. Emerging Market Monetary Policy and Currencies

Most currencies depreciated in the first half of 2024, especially those of
countries where real policy diﬀerentials have narrowed ...

... and heightened volatility has eroded the attractiveness of carry trades.

1. Currency Movements and Changes in the Diﬀerence Between

2. Emerging Market Carry, Adjusted for Volatility

2.5

0

−2.5

Domestic and US Real Policy Rates
(Percent)

Currency Q2 (right scale)
2024 H1 change in domestic and
US real policy rate diﬀerentials

Currency Q1 (right scale)
Currency YTD
(right scale)

d
n
a
l
o
P

s
e
n
p
p

i

i
l
i

h
P

u
r
e
P

o
c
i
x
e
M

a
i

b
m
o
l
o
C

d
n
a
l
i
a
h
T

h
c
e
z
C

c
i
l

b
u
p
e
R

a
i
s
e
n
o
d
n

I

a
i
n
a
m
o
R

y
r
a
g
n
u
H

l
i
z
a
r
B

a
i
d
n

I

e
l
i

h
C

h
t
u
o
S

a
c
i
r
f
A

15
10
5
0
−5
−10
−15

(Three-month implied yield diﬀerential versus US dollar rate,
divided by implied volatility)

Range since 2015

2023 Peak

Latest

1.5

1.0

0.5

0

−0.5

−1.0

a
i
d
n

I

a
i

b
m
o
l
o
C

o
c
i
x
e
M

l
i
z
a
r
B

h
t
u
o
S

a
c
i
r
f
A

a
i
s
e
n
o
d
n

I

y
r
a
g
n
u
H

d
n
a
l
o
P

e
l
i

h
C

d
n
a
l
i
a
h
T

a
i
s
y
a
l
a
M

s
e
n
p
p

i

i
l
i

h
P

Country-specific factors drove currency returns in 2024, even in high-carry
countries.

Markets expect emerging market central banks to be more in line with the
Federal Reserve.

3. Decomposition of Currency Returns

(Percent; solid bars refer to high-carry countries)

Idiosyncratic
2024 YTD Performance

Carry factor

Dollar factor

FX change

4. US Policy Rate Versus Changes in Emerging Market Policy Rates

(Number of central banks changing policy and percent)

LATAM net change
Asia net change

EMEA net change
US policy rate (percent, right scale)

2023 Performance

15

10

5

0

−5

−10

ZAR PLN INR MYR PEN RON CNH COP CLP MXN HUF IDR PHP THB BRL

−15

2000 02

04

06

08

10

12

14

16

18

20

22

24

15

0

−15
25
15
5
−5
−15

7

5

3

1

−1

−3

−5

Term premiums have mostly driven longer-term yields in emerging
markets ...

... and the spillover of changes in US term premium remains high, notably
for CEEMEA.

5. Changes in 10-Year Yields Since the April 2024 GFSR

6. Eﬀect of Changes in US Term Premium Across EM Regions

(Basis points)

(Proportion of variance explained)

175
125
75
25
−25
−75
−125
−175
−225

Average expected short term rate
Term premium
Change in yield

LATAM

CEEMEA

Asia

Average

BRA MYS THA COL MEX POL

IDN IND HUN CHL PER ZAF

2009

11

13

15

17

19

21

23

35

30

25

20

15

10

5

0

Sources: Bloomberg Finance L.P.; EUROPACE AG/Haver Analytics; IMF, World Economic Outlook database; national sources; and IMF staﬀ calculations.

Note: Panel 2 uses six-week averages. In panel 3, carry factor includes both interest rate diﬀerential and a global carry factor. The construction of the global carry factor and
the dollar factor follows Verdelhan (2018), using a portfolio of 16 EM and 9 advanced economy currencies. The decomposition is based on a rolling regression over
18 months. Panel 6 reports spillovers from changes in US term premiums to EM term premiums. Specifically, the measure of spillovers reported here—using the
methodology proposed by Diebold and Yilmaz (2009)—is the proportion of variation in EM term premiums that can be explained by shocks emanating from US term
premiums. EMs include 15 countries accounting for about 76 percent of total EM GDP. The spillovers shown here correspond to a 100-week rolling window. Data labels in
the figure use International Organization for Standardization (ISO) country codes. BRL = Brazilian real; CEEMEA = Central and Eastern Europe, the Middle East, and Africa;
CLP = Chilean peso; CNH = Chinese renminbi; COP = Colombian peso; EM = emerging market; EMEA = Europe, the Middle East, and Africa; FX = foreign exchange;
GFSR = Global Financial Stability Report; HUF = Hungarian forint; IDR = Indonesian rupiah; INR = Indian rupee; LATAM = Latin America; MXN = Mexican peso; MYR =
Malaysian ringgit; PEN = Peruvian sol; PHP = Philippine peso; PLN = Polish zloty; Q1 = first quarter; Q2 = second quarter; RON = Romanian new leu; THB = Thai baht;
YTD = year to date; ZAR = South African rand.

16

International Monetary Fund | October 2024

CHAPTER 1

STEAdYING ThE COuRSE: FINANCIAL MARkETS NAvIGATE uNCERTAINTY

United States (Figure 1.14, panel 4). Greater policy
alignment should stabilize interest rate differentials
between advanced economies and emerging markets.
That said, it may also increase the sensitivity of bond
yields in emerging markets to those in advanced
economies, both because expected policy paths will be
more synchronized and as a result of spillovers from
the term premium component that captures uncer-
tainty in interest rates. Increases in term premiums in
most emerging markets (Figure 1.14, panel 5), likely
resulting from larger spillovers from higher US term
premiums (Figure 1.14, panel 6), have primarily driven
recent changes in yields.

Portfolio Outflows Risks Have
Receded Somewhat

Portfolio flows to emerging markets have been

positive on net in recent months (Figure 1.15, panel 1).
Several countries, notably Egypt and Türkiye, have
experienced large inflows into local currency bonds amid
renewed investor optimism about the outlook despite
lingering debt challenges and elevated inflation, and
flows into Indian markets have benefited from India’s
inclusion in global bond indices. Conversely, equity
flows have been under pressure in some countries, which
may reflect concerns regarding the growth outlook or
political uncertainty in some cases. Year-to-date interna-
tional issuance of sovereign bonds has risen to its highest
level since 2021, although weak inflows into hard-cur-
rency bond funds suggest that market conditions could
become more challenging absent a turnaround.

The IMF’s capital-flows-at-risk measure indicates that

there is a 5 percent probability that emerging market
outflows could reach 2.4 percent of GDP over the
next three quarters, a marginal increase in outflow risk
since the April 2024 Global Financial Stability Report.
However, rising market volatility, as seen during the
early August shock, would materially increase outflows
risks if sustained over a longer period (Figure 1.15,
panel 2). Changes in the investor base have mitigated
the risks of portfolio outflows to some extent, as long-
term domestic investors like insurers and pension funds
have absorbed increasing shares of emerging market
bonds, likely serving as a stabilization force (see Box
1.4). Foreign investors appear to have become more
cautious about emerging market assets in aggregate, as
portfolio inflow cycles have become shorter and smaller
on average (Figure 1.15, panel 3). Global factors—such

as the interest rate environment or geopolitical uncer-
tainty—may continue to affect the relative attractiveness
of cross-border investment in emerging markets. Indeed,
dedicated emerging market bond and equity funds
domiciled in the United States have experienced cumu-
lative outflows since March 2022 (Figure 1.15, panel 4).

Emerging Markets with Weaker Fiscal
Buffers Could Face More Constrained
Funding Conditions

Although many emerging markets have experienced
lower financing costs in recent years, investors continue
to be attuned to these markets’ fiscal sustainability. After
progress following the pandemic, the momentum on
fiscal consolidation has waned, and market analysts’
consensus expectations regarding the budget balance for
the aggregate government in 15 major emerging markets
over the next three years have become more pessimistic
and are firmly in deficit territory (Figure 1.16, panel 1),
with 11 of these countries set to underperform13 ana-
lysts’ forecasts for fiscal year 2024.

Some sovereigns could be ensnared in a “debt begets

more debt” quandary, especially considering that still-
high global interest rates, larger financial spillovers
from advanced economies, and weaker prospects in
regard to longer-term economic growth are making it
more difficult to service existing debt. To avoid such
an outcome, these sovereigns need to improve their
primary balances. And yet many emerging markets are
operating well below their long-term fiscal buffers,14

13Compared to analysts’ consensus estimates made in the third

quarter of 2022.

14The concept of fiscal buffers is motivated by the primary
balance space, as described in the April 2024 Fiscal Monitor. The
debt-stabilizing primary balance for the contemporaneous year can

rt – gt
1 + gt

 × dt – 1, given the values of the nominal
be defined as P*t =
effective interest rate (rt) and growth rate ( gt). In this context, the
long-term debt-stabilizing primary balance is simplified as

P* =

r – g*
1 + g*

 × d, with the assumption that the effective steady-state

long-term interest rate (r) is equivalent to the nominal forward
five-year yield in five years, implied by the rate on current
on-the-run government bonds, adjusted by differences in term
premiums. The interest rate is also weighted by outstanding
local- and foreign-currency-denominated debt and takes into account
the cost arising from annualized depreciation of the external debt
based on historical long-term data (January 2000 to July 2024).
Long-term nominal growth (g) is derived from World Economic
Outlook estimates, and gross debt (d ) is based on the prevailing gross
government debt level as of the end of 2023. The 2024 fiscal buffer
is estimated by subtracting the long-term debt-stabilizing primary
balance from the expected 2024 primary balance.

International Monetary Fund | October 2024

17

GLOBAL FINANCIAL STABILITY REPORT: STEAdYING ThE COuRSE: uNCERTAINTY, ARTIFICIAL INTELLIGENCE, ANd FINANCIAL STABILITY

18

International Monetary Fund | October 2024

Equities, Korea and Taiwan Province of ChinaEquities, EM ex ChinaLocal currency bonds,EM ex ChinaEquities, ChinaLocal currency bonds, ChinaProbability of nonresident portfolio outflows to GDP over the next 3 quarters5th percentile of flows (right scale)Local currency bond inflowsEquity inflowsMedian, across countries, number of months with bond inflows over last 12 months (right scale)Cumulative flow: EquityCumulative flow: BondCumulative flow to AUM:Equity (right scale)Cumulative flow to AUM:Bond (right scale)Figure 1.15. Emerging Market Portfolio FlowsPortfolio flows have remained positive on net, though with considerable diﬀerentiation.1. Portfolio Flow Tracking: Local Currency Bonds and Equities(Billions of US dollars)−2040−10010203005010203040−2040−10010203003.50.51.01.52.02.53.0Capital-flows-at-risk worsened modestly; risks appear slightly higher than average.2. Capital Flows at Risk(Percent of GDP)Portfolio flow cycles have become shorter in recent years.3. Portfolio Inflow Cycles(Cumulative equity and local currency bond inflows before outflows,billions of US dollars, based on monthly data)0270306090120150180210240−450−40−35−30−25−20−15−10−5Dedicated emerging market bond funds domiciled in the United States have seen large outflows since 2022.4. Flows in US-Domiciled Emerging Market Mutual Funds andExchange-Traded Funds(Billions of US dollars)−180−16−14−12−10−8−6−4−201248Jan. 2023Jan. 24Apr. 23Apr. 24Jul. 23Jul. 24Oct. 23Jan. 2023Jan. 24Apr. 23Apr. 24Jul. 23Jul. 24Oct. 23Mar. 2018Mar. 19Mar. 20Mar. 21Mar. 22Mar. 23Sep. 18Sep. 19Sep. 20Sep. 21Sep. 22Sep. 23Mar. 24Sep. 242012131415161718192021222324Mar. 2022Aug. 22Jan. 23Jun. 23Nov. 23Apr. 24Sources: Bloomberg Finance L.P.; EPFR; EUROPACE AG/Haver Analytics; IMF, World Economic Outlook database; national sources; and IMF staﬀ calculations.Note: Panel 1 reports data separately for Korea and Taiwan Province of China because the IMF does not classify them as emerging markets. “EM ex China” includes an unbalanced sample of 20 emerging markets. Daily data on Chinese equity flows ceased being available as of August 11. In panel 2, “portfolio flows at risk” is defined as the 5th percentile of the three-quarters-ahead nonresident portfolio flows’ probability density. Panel 3 includes monthly data on 16 countries for equity flows and 20 countries for bond flows. Inflow episodes are reset at the first monthly occurrence of outflows. AUM = assets under management; EM ex China = emerging markets excluding China.CHAPTER 1

STEAdYING ThE COuRSE: FINANCIAL MARkETS NAvIGATE uNCERTAINTY

International Monetary Fund | October 2024

19

Small and worsening rangeSmall and worsening averageLarge or improving rangeLarge or improving averageBBBABBAABBB with worseningFY24 primary balanceRatings drift, trailing six months (right scale)1st to 10th percentiles1st to 25th percentilesMedianGroup of Twenty EMsCCC ratedSpread >10%Return to B (avg)Return to B (max)BB 2024IG 2024CCC andbelow 2024B 2024BB 2019IG 2019CCC andbelow 2019B 2019CEEMEAAsiaLATAM2022:Q3 expectationsEM average2023:Q3 expectationsFigure 1.16. Emerging Market Fiscal Buﬀers and Financial CostsWith fiscal consolidation delayed, deficits have remained above prepandemic levels.While some emerging markets have room for fiscal expansion, most face higher spreads.1. Overall Fiscal Balances and Analyst Expectations(Percent of GDP)2. Five-Year Credit Default Swap Prices and 2024 Fiscal Buﬀers Estimates(Basis points; percent of GDP)025050100150200−10−80−6−4−2Sovereign risk premiums for emerging markets rose with worsening fiscal buﬀers ...3. Pricing of Sovereign Credit Risk(Cumulative change in five-year credit default swap z-scores since 2023:Q3)−1.01.0−0.500.5CC andbelowACCCBBBBBB... and continued fiscal underperformance could precede rating reassessment and “cliﬀ eﬀects.”4. Average Credit Ratings and Ratings Drift(Rating by group; trailing six-month percentage)−6040−40−20020Downgrades to the B-rating band could push vulnerable EMs closer to the brink of losing market access.5. Distribution of EM Sovereigns by Hard-Currency Spreads(Number of sovereigns by spread range)020481216Recently, it has been taking defaulted sovereigns longer to return to B ratings or better.6. Timeline for Recently Defaulting Sovereigns(Average months to and after default, t = 0)201415161718192021222324f25f26f−6Sources: Bloomberg Finance L.P.; Fitch Ratings; JPMorgan; Moody’s Investor Services; S&P Global; and IMF staﬀ calculations.Note: Data in panel 1 consist of those for 15 major EM sovereigns whose fiscal balance trajectories are tracked by broad analysts and for which expectations for a two-year forward-looking horizon are available. Sample in panels 2 and 3 includes 16 major EM sovereigns with outstanding external debt denominated in US dollars, with eight in each category. Panels 4 and 5 include a sample of 80 EM sovereigns that were continuously rated by at least one of three international rating agencies from December 2008 to August 2024. The sample excludes sovereigns whose ratings were withdrawn but includes those under default. “Ratings drift” is defined as total net change (as a percentage of total) in credit ratings by the three agencies over the preceding six months. Positive ratings drift is in green, while negative ratings drift is in red. avg = average; CEEMEA = Central and Eastern Europe, the Middle East, and Africa; EMs = emerging markets; f = forecast; FY = fiscal year; LATAM = Latin America; Q3 = third quarter.−4−202468200811141720230―2%2―4%4―6%6―8%8―10%>10%ResolveddefaultersUnresolveddefaultersRatings−40−200204060801002020s (n = 6)2010s (n = 2)2020s (n = 2)2010s (n = 4)2000s (n = 5)Market access likely toremain challenging30 Sep. 202331 Dec. 2331 Mar. 2430 Jun. 2430 Sep. 24GLOBAL FINANCIAL STABILITY REPORT: STEAdYING ThE COuRSE: uNCERTAINTY, ARTIFICIAL INTELLIGENCE, ANd FINANCIAL STABILITY

with primary balances for the 2024 fiscal year failing to
meet the requirements to achieve longer-term debt-sta-
bilizing primary balances. Financial markets appear
to have differentiated countries along this dimension:
Emerging markets with worse fiscal buffers gener-
ally have higher credit default spreads (Figure 1.16,
panel 2), and these spreads are diverging between
countries with “large or improving” and “small and
worsening”15 buffers (Figure 1.16, panel 3). Nonethe-
less, with most sovereigns’ fiscal buffers still hovering
within a reasonable range,16 many are still within reach
to steer toward a more sustainable debt pathway.

Increased pricing of emerging market sovereign
risks could also reflect fears of a reassessment in credit
ratings. Ratings downgrades are susceptible to “cliff
effects,” for which downgrades can be extreme and
can further constrain funding conditions owing to
incorporation of ratings into regulations and risk
limits (Figure 1.16, panel 4; see also Chapter 3 of
the October 2010 Global Financial Stability Report).
Worryingly, sequential downgrades involve a risk that
even at ratings a notch or two above the “near default:
CCC ratings” threshold, some sovereigns may already
find themselves on the verge of losing market access
(Figure 1.16, panel 5), as historical ratings transitions
indicate a significant likelihood of default.17 Critically,
more sovereigns are finding themselves in that situation
now relative to the number in 2019,18 and the IMF’s
sovereign Debt-at-Risk framework (see the October
2024 Fiscal Monitor) estimates that debt risks may
heighten further under high economic uncertainty.

15The relative risk-pricing differentiation among the sample group
is measured using a normalized z-score methodology. “Small or wors-
ening” sovereigns are identified as those with fiscal buffers beyond a
deficit of 2 percent, and “borderline” sovereigns have fiscal buffers
ranging from –2 to 2 percent and expect to have widening fiscal year
2024 primary deficits. “Large or improving” sovereigns are those
with large fiscal buffers (exceeding 2 percent), as well as borderline
sovereigns expected to experience narrowing fiscal year 2024 primary
deficits.

16A “reasonable rate” is within the –2 to 2 percent range, as the
average five-year standard deviation of sample sovereigns’ primary
balances is about 2 percent of GDP (based on expectations from
fiscal year 2020 to fiscal year 2024).

17B-rated sovereigns have a cumulative default rate of up to 17

percent over a period of five years, based on historical five-year
issuer-weighted rating transition studies for sovereigns based on Fitch
ratings (1995 to 2023), Moody’s issuer ratings (1983 to 2023), and
S&P foreign currency ratings (1975 to 2023). The Moody’s study
also indicates that ratings of defaulted sovereigns, on average, tend to
be in the B-rating range one year before a default event.

18Of the 80 sovereigns sampled, 17 (21 percent) have average rat-
ings at CCC+ or worse, compared with 4 (5 percent) in December
2019.

20

International Monetary Fund | October 2024

And markets often front-run ratings actions, with
some defaulting sovereigns’ spreads having exceeded
10 percent before a downgrade to a rating band of
CCC or lower (Figure 1.16, panel 6).19 More con-
cerning is the recent postpandemic trend wherein
many defaulted sovereigns are experiencing an
extended duration in rating bands of CCC or worse,
with their prolonged stays in that range reflecting
ongoing external and domestic challenges and a prob-
able necessity for persistent fiscal reforms (see Kogan
and others 2024). The continued struggle for market
confidence and access underscores how important it
is for emerging market sovereigns, especially during
periods of strong growth, to maintain sufficient fiscal
buffers and flexibility to mitigate effects of unexpected
shocks.

Frontier Markets Are Still Grappling with High
Borrowing Costs

Frontier sovereign spreads20 have followed global

trends and tightened further in the second quar-
ter, having approached long-term average levels
(Figure 1.17, panel 1). Significant progress on debt
restructuring has also helped lift investor sentiment
toward frontier markets. For example, the Eurobond
restructurings in Suriname, Zambia and Ghana were
completed in December, June, and October, respec-
tively, while an agreement in principle was reached
with creditors in Sri Lanka in September. Policy
actions by local authorities have also resulted in posi-
tive developments; for example, in Nigeria, rate hikes
and the clearing of overdue domestic central bank for-
eign exchange obligations have helped the naira show
more signs of stability.

Against this backdrop, frontier economies contin-

ued to issue international debt in the second quar-
ter, although yields remained high. Some frontier
economies and low-income countries took advantage
of strong investor risk appetite to issue sovereign
bonds after a lengthy hiatus. However, although
just 14 percent of frontier economies have sovereign
spreads above 1,000 basis points—a lower share

19An examination of default events since 2020 suggests that
hard-currency spreads for 12 out of a sampled 19 defaulting sover-
eigns exceeded 10 percent before a downgrade to CCC or worse.

20The “frontier market” classification comprises 43 countries that
either are included in the JPMorgan Next Generation Market index
or, if not included in that index, are low-income countries with
international bond issuance.

CHAPTER 1

STEAdYING ThE COuRSE: FINANCIAL MARkETS NAvIGATE uNCERTAINTY

Figure 1.17. Frontier Market Developments

Sovereign spreads have tightened for frontiers, but yields remain high.

1. Frontier Market Spreads and Yields

(Basis points; percent)

A large proportion of debt maturing in coming months is trading close to
or above 10 percent.

2. Coupon at Issue Versus Current Yield of Eurobond Maturities

(Millions of US dollars; yields)

25th to 75th percentile
Share with yields
close to 10 percent or higher
Share with spread
above 1,000 bps

80

70

60

50

40

30

20

10

0
2002 04

06

08

10

12

14

16

18

20

22

24

1,500

1,300

1,100

900

700

500

300

100

−100

Upcoming maturity
Of which currently trading close to 10 percent yield
or higher

1
Q
:
5
2

3
Q
:
5
2

1
Q
:
6
2

3
Q
:
6
2

3
Q
:
4
2
0
2

2
Q
:
5
2

4
Q
:
5
2

2
Q
:
6
2

4
Q
:
6
2

4
Q
:
4
2
0
2

3
Q
:
4
2
0
2

1
Q
:
5
2

3
Q
:
5
2

1
Q
:
6
2

3
Q
:
6
2

2
Q
:
5
2

4
Q
:
5
2

2
Q
:
6
2

4
Q
:
6
2

4
Q
:
4
2
0
2

3
Q
:
4
2
0
2

1
Q
:
5
2

3
Q
:
5
2

1
Q
:
6
2

3
Q
:
6
2

SSA

Europe and
Central Asia

South Asia

LAC

MENA

4,000
3,500
3,000
2,500
2,000
1,500
1,000
500
0

The maturity of eurobond issuances has declined over time and remains
relatively low, indicating higher upcoming refinancing needs.

Median public debt levels are high by historical standards, but
interest-to-revenue ratios are much higher in frontiers, indicating lower
capacity to service debt.

3. Maturity Distribution of Frontier Market Eurobond Debt Issuance
(Distribution of maturities frontier market Eurobond issuances, percent)

4. Median Fiscal Metrics

(Percent of GDP; percent of fiscal revenue)

Under 7y

7 to 15y

15 to 30y

Over 30y

Average WAM

100

80

60

40

20

0

2010 11 12 13 14 15 16 17

18

19

20

21

22

23

24

20

15

10

5

–

100
90
80
70
60
50
40
30
20
10
0

Public debt/GDP—Frontiers
Public debt/GDP—EMBIG ex-frontier
Interest/revenue—Frontiers (right scale)
Interest/revenue—EMBIG ex-frontier
(right scale)

Forecast

3
0

4
0

5
0

6
0

7
0

8
0

9
0

0
1

1
1

2
1

3
1

4
1

5
1

6
1

7
1

8
1

9
1

0
2

1
2

2
2

3
2

4
2

5
2

6
2

7
2

8
2

9
2

2
0
0
2

20

15

10

5

0

Sources: Bloomberg Finance L.P.; EUROPACE AG/Haver Analytics; IMF, World Economic Outlook database; JPMorgan; and IMF staﬀ calculations.

Note: Panel 1 shows the 25th and 75th percentiles of the JPMorgan Next Generation Market Index. Panel 3 shows the weighted average maturity of international debt
issuance by frontier economy sovereigns. bps = basis points; EMBIG = JPMorgan Emerging Market Bond Index Global; LAC = Latin America and the Caribbean;
MENA = Middle East and North Africa; SSA = sub-Saharan Africa; WAM = weighted average maturity.

than a year ago—roughly a fifth of frontier econo-
mies still have yields close to 10 percent or higher,
a materially larger share than the long-term average.
Significant amounts of frontier debt are coming due
in the remainder of 2024 (roughly $4 billion) and in
2025 and 2026 (roughly $13 billion and $14 billion,
respectively), with roughly 60 percent of maturing
bonds issued by countries with prevailing yields close
to or above 10 percent, notably frontier economies
in South Asia, and sub-Saharan Africa (Figure 1.17,
panel 2).

A decline in global interest rates would alleviate
refinancing pressures for these frontier economies,

although the decline in the weighted average matu-
rity of frontier debt issuance—that is, reliance on
shorter-term debt—makes frontiers more exposed to
gyrations in expectations regarding monetary policy, as
refinancing would occur more frequently (Figure 1.17,
panel 3). More fundamentally, debt-to-GDP ratios for
both emerging market and frontier economies remain
well above historical average levels. Under IMF staff
projections, these debt levels are not expected to come
down meaningfully in the medium term, and interest
repayment burdens for frontier economies are pro-
jected to ease somewhat but remain relatively high in
the medium term (Figure 1.17, panel 4).

International Monetary Fund | October 2024

21

GLOBAL FINANCIAL STABILITY REPORT: STEAdYING ThE COuRSE: uNCERTAINTY, ARTIFICIAL INTELLIGENCE, ANd FINANCIAL STABILITY

Adaptation and Mitigation Can Strengthen
Emerging Markets’ Climate Resilience

Global issuance of sustainable debt rebounded in the

first half of 2024. Green bonds remained the largest
component, accounting for roughly half of sustain-
able debt issuance and exceeding the amount issued
in the first six months of past years. The share of
issuance by emerging markets has somewhat declined
recently (Figure 1.18, panel 1). Moreover, issuance
of sustainable debt continues to account for a rela-
tively small portion of total debt issuance in emerging
markets, even though the share of offshore issuance
of sustainable debt in total issuance of sustainable
debt is somewhat higher (Figure 1.18, panel 2), likely
reflecting that demand for sustainable bonds from
emerging markets originates from investors based in
advanced economies who prefer hard-currency over
local-currency debt. Underinvestment in climate
change mitigation and adaptation in emerging market
and developing economies could lead to global risks to
financial stability through greater exposure to systemic
climate-related financial risks, including contagion
effects along value chains (see Chapter 2 of the Octo-
ber 2022 Global Financial Stability Report).

22

International Monetary Fund | October 2024

Different estimates suggest that about 75 to
90 percent of climate finance flows are directed
toward mitigation efforts (CPI 2023; OECD 2023;
UNEP 2023), even though there is growing aware-
ness that investing in climate adaptation, in addition
to mitigation, is both an inevitable and necessary
priority. Specifically, mitigation is focused on reducing
or eliminating the emission of greenhouse gases to
limit further climate change, but adaptation finance
is aimed at assisting communities and ecosystems to
cope with climate change impacts already occurring or
expected to occur. Emerging market and developing
economies are disproportionately affected by both
climate change and a lack of adaptation investment
despite historically contributing the least to green-
house gas emissions, while international adaptation
finance flows to developing countries are 10 to 18
times below estimated needs, and the gap is widening
(UNEP 2023). A significant portion of private sector
capital providers are unfamiliar with the adaptation
investment thesis, and even among those who are
familiar, the perceived risk remains prohibitively high.
Among private sector investors, mitigation is typically
seen as an opportunity, whereas adaptation is often

UAEBRACHLCHNINDIDNMEXZAFTHATURSustainability-linked bondSocial bondGreen bondSustainability-linked loanSustainability bondGreen loanShare of EMs in total (right scale)Figure 1.18. Update on Sustainable Debt Issuance by Emerging MarketsGlobal sustainable debt issuance has improved this year, led by record green bond issuance.1. Global Sustainable Debt Issuance by Instrument(Billions of US dollars; percent of total)0100200300400500Billions of US dollars048121620Percent2019:Q119:Q320:Q120:Q321:Q121:Q322:Q122:Q323:Q123:Q324:Q124:Q3(norm)Emerging market sustainable debt makes up a relatively larger share in oﬀshore markets than in total debt issuance.2. Country-Level Sustainable Bond Issuance in Emerging Markets as aShare of Total Domestic and Oﬀshore Issuance(Cumulative percentage since 2015)30 0102000.51.01.52.02.53.03.5Proportion of sustainable bond issuancein total issuanceProportion of oﬀshore sustainable bond issuancein total oﬀshore issuance Sources: Bloomberg Finance L.P.; BloombergNEF; and IMF staﬀ calculations.Note: In panel 1, the share of EMs shows the four-quarter moving average of total issuance in EMs as a percentage of global issuance, and the third quarter of 2024 value is based on the latest available information as of August 2024. “24:Q3 (norm)” refers to the normalized value for the third quarter of 2024, based on issuances during July–August 2024. Data labels in panel 2 use International Organization for Standardization (ISO) country codes. EMs = emerging markets.CHAPTER 1

STEAdYING ThE COuRSE: FINANCIAL MARkETS NAvIGATE uNCERTAINTY

viewed as a government responsibility, too complex, or
lacking clear metrics or sufficient investment returns
(IMF, forthcoming). To date, tracked adaptation
finance is dominated by public actors (98 percent)
(Climate Policy Initiative 2023). While three out of
five surveyed private financial institutions intend to
increase their allocation to adaptation investments,
they also highlight multiple barriers to investment and
call for more product innovation and public–private
partnership to unlock capital for adaptation, practical
investment guidance, and investor-relevant metrics
(Standard Chartered Bank 2024).

Slowing Growth and Deflationary Pressures
Weigh on China’s Financial System

China continues to experience deflationary pressures

amid slowing demand both domestically and from
external markets. Policy support across the monetary,
fiscal, and housing fronts continues to be measured so
far and appears unable to offset the drag that housing
market adjustment, now heading into its fourth year,
is exerting on business and consumer confidence. In
addition, slowing global growth and rising pressures
related to fragmentation may weigh on the export
sector, the key driver of growth in recent quarters.
Against this backdrop, expectations regarding inflation
continue to decline, with one-year-ahead expected con-
sumer price index inflation having nearly halved from
a year ago, to 1.3 percent, and the probability that it
will fall below its current level of 0.3 percent has also
increased (Figure 1.19, panel 1). Coupled with a hous-
ing market turnaround not yet in sight—home price
declines have accelerated again recently, with primary
and secondary home prices down 7 and 13 percent
from their peaks, respectively, and primary market sales
40 percent lower than their prepandemic peak—these
pressures call for more decisive and vigorous policy
support.

Recent declines in government bond yields reflect
the downbeat sentiment. Both the 2- and 10-year cen-
tral government bond yields have fallen to near record
lows. The compression of term premiums (Figure 1.19,
panel 2), especially those for longer-term rates, indi-
cates a weaker economic outlook and flight to safety,
as returns on other assets like housing and stocks have
continued to disappoint. The outperformance of stocks
in defensive and high-dividend sectors, like utilities
and energies, also points to low appetite for risk
(Figure 1.19, panel 3).

At the end of September, Chinese authorities

unveiled a series of monetary and regulatory stimulus
measures aimed at bolstering the domestic economy
and stabilizing the property sector and consumer
sentiment. The announcement initially triggered a
strong appreciation in stock prices, which was partially
retraced in subsequent days as investors reportedly
await details on potential fiscal stimulus measures
viewed as crucial in addressing the structural challenges
faced by the Chinese economy.

A decline in benchmark bond yields has also driven

other bond yields lower, led by local government
financing vehicles debt following the fiscal support for
financially weak regions. Institutions like retail-focused
wealth management products and mutual funds have
displayed a strong appetite for fixed income assets.21
In addition, foreign investors have also increased their
holdings of renminbi-denominated bonds, particularly
negotiable certificates of deposit in the interbank mar-
ket (Figure 1.19, panel 4). A sudden rise in bench-
mark bond yields could trigger a sharp repricing in
the broader fixed income markets, redemptions from
investment funds, and significant market volatility.
The performance of China’s banking system in
this challenging environment is crucial to financial
stability. Asset quality will continue to deteriorate if
policy support fails to restore growth momentum,
and weak credit demand is weighing on lending
volumes and profit margins. So far, despite financial
troubles in the property-related sectors, including
local government financing vehicles, banks reported
nonperforming loan (NPL) ratios have remained low.
Low mortgage defaults (NPL ratios less than 1 per-
cent) and manageable direct exposures to developers
(less than 6 percent of total bank loans) have allevi-
ated pressures. Importantly, banks have been proac-
tive in addressing nonperforming assets (NPAs22),
with write-offs and disposals topping 3 trillion yuan
each year since 2020 (Figure 1.20, panel 1). Since
2012, the cumulative reported NPLs amounted to
less than 3 trillion yuan in 2023, and write-off and

21Over the past few months, the Chinese authorities have

issued repeated warnings against interest rate risks and have taken
preemptive measures, including administrative interventions, to
guide smaller financial institutions to reduce their bond exposures.
On August 31, the central bank announced to have conducted sec-
ondary market transactions in August by buying short-term central
government bonds and selling long-term central government bonds,
resulting in a net liquidity injection of 100 billion yuan.

22NPA includes nonloan assets and is broader than NPLs. However,

disclosure of NPA ratio is limited.

International Monetary Fund | October 2024

23

GLOBAL FINANCIAL STABILITY REPORT: STEAdYING ThE COuRSE: uNCERTAINTY, ARTIFICIAL INTELLIGENCE, ANd FINANCIAL STABILITY

Figure 1.19. Inflation and Asset Developments in China

Deflation pressure is intensifying amid weak demand from home and
abroad.

Government bond yields have dropped to record lows, with compressing
term premiums on flight to safety driving the decreases.

1. Distribution of Analysts’ Forecasts for One-Year-Ahead

2. Decomposition of Changes in Yields on Central Government Bonds

Headline Inflation
(Probability density)

Oct. 2023 GFSR

Apr. 2024 GFSR

Latest

−0.2

0.3

0.8

1.3

1.8

2.3

2.8

1.0

0.8

0.6

0.4

0.2

0
−0.7

High-dividend and defensive stocks have outperformed.

(Cumulative changes since October 2023, basis points)

2-year CGB yield

10-year CGB yield

Averaged expected
short rate
Term premium

20

0

−20

−40

−60

−80

−100

Oct.
2023

Jan.
24

Apr.
24

Jul.
24

Oct.
2023

Jan.
24

Apr.
24

Jul.
24

Strong investor appetite from foreign investors as well as others has
fueled a bond rally.

3. Performance by MSCI China Sectors in 2024

(Percent)

4. Foreign Holdings by Instrument

(Billions of renminbi)

30

20

10

0

−10

−20

−30

−40

Valuation
Earnings
Returns

Government bonds
Bank commercial papers
(NCD)

Policy bank bonds
Market share of foreign
holdings

3.0%

5.6%
4%

m
m
o
C

s
k
n
a
B

e
c
n
a
r
u
s
n

I

y
g
r
e
n
E

s
e
i
t
i
l
i
t

U

s
l
a
i
r
t
s
u
d
n

I

.
c
s
i
D

.
s
n
o
C

s
l
a
i
r
e
t
a
M

T
I

e
r
a
C
h
t
l
a
e
H

s
e
l

p
a
t
S
.
s
n
o
C

e
t
a
t
s
E
l
a
e
R

Jan.
2022

Jul. 22

Jan. 23

Jul. 23

Jan. 24

Jul. 24

10

0

−10

−20

−30

−40

−50

−60

−70

450

400

350

300

250

200

150

100

50

0

Sources: Bloomberg Finance L.P.; EUROPACE AG/Haver Analytics; and Thomson Reuters.

Note: In panel 2, decomposition of bond yields into expected short rate and term premiums follows Adrian, Crump, and Moench (2013). In panel 3, the decomposition is
based on 12-month-forward measures of valuation and earnings. CGB = central government bond; Comm. = communications; Cons. disc. = consumer discretionary;
GFSR = Global Financial Stability Report; IT = information technology; NCD = negotiable certificates of deposit.

disposal totaled 22 trillion, which have effectively
lowered banks’ headline NPL ratios by 1.5 percentage
points.23 Disposals have been done mainly through
transferring NPAs to state-owned asset management
companies (AMCs) in the primary market, while the
secondary market—non-AMC buyers of NPAs—
remains nascent. Disclosures from listed AMCs show

23The adjusted NPL ratios are likely still underestimating asset
quality risks given the frequent use of regulatory forbearance and
systematic perceptions of implicit guarantees.

24

International Monetary Fund | October 2024

the bulk of NPA acquisitions in 2023 originating
from the property market, small and medium enter-
prises, and local government financing vehicle–related
sectors (Figure 1.20, panel 2).

The key question is whether AMCs will continue

to have the capacity to absorb problem assets with
their balance sheets weakening. The four national
AMCs established in 1999 (80 percent market share),
which mainly serve state-owned and joint-stock banks,
dominate the primary market for NPAs, along with
more than 50 regional AMCs established since 2015

CHAPTER 1

STEAdYING ThE COuRSE: FINANCIAL MARkETS NAvIGATE uNCERTAINTY

Figure 1.20. Drill-Down on Chinese Asset Management Companies

Banks have relied on write-oﬀs and disposals to manage bad debt.

Disposals have focused on property and LGFV-related nonperforming
loans.

1. Cumulative Changes in Banks’ Nonperforming Loans, Write-Oﬀs, and

2. Acquisitions of Nonperforming AMCs by Sector, 2023

Other Disposals Since 2012
(Percent; trillions of renminbi)

Reported NPL

Write-oﬀ

Other disposal

NPL % adjusted for write-oﬀ
and other disposal (left)

Reported NPL % (left)

2012

14

16

18

20

22

24E

5

4

3

2

1

0

(Percent, based on disclosure of the largest AMC)

25

20

15

10

5

0

Real estate

Manufacturing

Leasing & commercial
services

Wholesale &
retail trade

Construc...

Other

Public fac...

AMCs’ fundamentals have weakened since the pandemic and the property
downturn.

Failure of AMCs could generate macrofinancial instability.

3. Equity and Profitability of AMCs

(Three-year average percent of total assets)

s
t
e
s
s
a
o
t
y
t
i

u
q
E

s
t
e
s
s
a
n
o
n
r
u
t
e
R

50
40
30
20
10
0

2
1
0
−1
−2
−3

National AMCs
Regional AMCs (asset weighted)

0

20

40

60

80

100

0

20

40

60

80

100

Asset share 2018

Asset share 2023

4. Composition of Assets and Liabilities of AMCs

100
90
80
70
60
50
40
30
20
10
0

(Percent)

Other

LT
investment

Trading
securities

Receivables

Cash

National

Regional

Assets

Other

LT debt

ST debt

Other
current
liabilities

National

100
90
80
70
60
50
40
30
20
10
0

Regional

Liabilities

Sources: China, National Financial Regulatory Administration; company disclosures; EUROPACE AG/Haver Analytics; and S&P Capital IQ Pro.

Note: In panel 1, “other disposal” is disclosed periodically, and the value is imputed for some years based on actual volume of nonperforming loans. AMC = asset
management company; E = estimate; LGFV = local government financing vehicle; LT = long term; NPL = nonperforming loan; ST = short term.

that target smaller banks in their regions.24 In previous
years, the national AMCs have grown into conglomer-
ates through networks of subsidiaries offering services
beyond NPA acquisition, such as lending, trust,
insurance, brokerage, and real estate. The fundamentals
of the national AMCs have weakened since 2018, as
profitability from both NPAs and other business lines
has suffered as a result of the pandemic, the property
market downturn, and in some cases, overexpansion.
Capital levels, as proxied by equity-to-asset ratios, have
dropped to distressed levels of below 5 percent at two

of the national AMCs (Figure 1.20, panel 3). Based
on disclosures from a limited sample, regional AMCs
appear more resilient on these two measures, likely
reflecting more confined business models,25 though
limitation on the availability and granularity of disclo-
sures warrants caution about this finding. Moreover, in
the near term, regional AMCs are unlikely to fill any
gap left by their national peers.

Financial distress in China’s AMCs could generate
macrofinancial instability. They are intertwined with
the rest of the country’s financial system through

24A fifth national AMC, established in 2020, remains small, hold-

25Regional AMCs have not expanded beyond the NPA market

ing less than 0.2 percent of total AMC assets.

and are required to operate within their jurisdictions.

International Monetary Fund | October 2024

25

GLOBAL FINANCIAL STABILITY REPORT: STEAdYING ThE COuRSE: uNCERTAINTY, ARTIFICIAL INTELLIGENCE, ANd FINANCIAL STABILITY

Figure 1.21. Corporate Market Dynamics

US corporate bond valuations remain stretched,
though they have eased somewhat.

Foreign-exchange-hedged US corporate bonds
are attractive to Japanese investors.

The syndicated loan market has become active
recently, regaining share from private credit
direct lenders.

1. Misalignments in Corporate Bond Spreads

2. Foreign-Exchange-Hedged Yield of US

3. Syndicated Loans and Direct-Lending

2
1
0
−1
−2
−3
−4
−5
−6
−7
−8

(Number per unit of risk; percentiles)

Misalignment per risk unit
Percentile (right scale)

1
Q
:
4
2

3
Q
:
4
2

4
Q
:
2
2
0
2

1
Q
:
4
2

3
Q
:
4
2

4
Q
:
2
2
0
2

1
Q
:
4
2

3
Q
:
4
2

4
Q
:
2
2
0
2

1
Q
:
4
2

3
Q
:
4
2

4
Q
:
2
2
0
2

100
90
80
70
60
50
40
30
20
10
0

Investment-Grade Corporate Bonds and
Japanese Government Bonds
(Percent)

Substitution
(Billions of US dollars)

JGB 10-year
US IG corporate bonds
Treasury 10-year

2.5

2.0

1.5

1.0

0.5

0

−0.5

DL refinanced by BSL
BSL refinanced by DL

2023:
Q1

23:
Q2

23:
Q3

23:
Q4

24:
Q1

24:
Q2

12

10

8

6

4

2

0

US IG

US HY

Euro
area IG

Euro
area HY

Jan.
2022

Jul.
22

Jan.
23

Jul.
23

Jan.
24

Jul.
24

Sources: Bank for International Settlements; Bloomberg Finance L.P.; EUROPACE AG/Haver Analytics; JPMorgan; PitchBook Data, Inc.; Thomson Reuters DataStream; and
IMF staff calculations.

Note: In panel 1, misalignment is the difference between market spread and model-based spread scaled by the standard deviation of monthly changes in spread. Negative
values indicate overvaluation. For the model details, please see the October 2019 Global Financial Stability Report, Online Annex 1.1. Intuitively, this measure indicates
how many standard deviations of monthly changes in spread (or “risk units”) it would take for the spreads to get back to fair value. “US” valuations are based on the
Bloomberg US corporate bond index and includes non-US issuers. “Euro area” valuations are based on the Bloomberg Euro Corporate Bond Index and includes non–euro
area issuers. Panel 3 shows yields hedged in Japanese yen. BSL = broadly syndicated loans; DL = direct lending; HY = high yield; IG = investment grade; JGB = Japanese
government bond.

investments, lending (or receivables), and reliance on
bank and market financing (Figure 1.20, panel 4). A
credit event at a large AMC would hamper a source
of NPA disposal for banks, putting some banks at
risk. Distress in one national AMC generated signif-
icant ripple effects in the financial system, requiring
a $6.6 billion state-led bailout in 2021. The Chinese
authorities have strengthened regulations on AMCs in
recent years by, among other things, centralizing the
supervision of local AMCs under the National Finan-
cial Regulatory Administration.

Rising asset quality and profitability pressures create

additional challenges to the banking sector’s capacity
to manage bad debt on its own. Asset quality risk
will continue to rise if policy support fails to restore
growth momentum. Profitability pressures will remain
in the near term as weak credit demand and downbeat
sentiment continue to weigh on business volumes and
profit margins.

Corporate Credit
Debt Servicing Remains Challenged for Many
Firms Even with Monetary Policy Easing

Investor optimism that the global economy will
achieve a soft landing has helped keep corporate bond
spreads tight (see Figure 1.7, panels 2 and 3). How-
ever, the misalignment in corporate bond valuation,
based on a model that account for macro fundamen-
tals, has remained at levels similar to those at the time
of the April 2024 Global Financial Stability Report. The
degree of overvaluation among US issuers is elevated
by historical standards (Figure 1.21, panel 1). In
addition to confidence in the global economy, strong
demand from overseas investors drove valuation up; for
instance, Japanese investors have reportedly preferred
US investment-grade corporate bonds to Treasury
securities because the yields on the former more than
compensate for costs associated with foreign exchange

26

International Monetary Fund | October 2024

CHAPTER 1

STEAdYING ThE COuRSE: FINANCIAL MARkETS NAvIGATE uNCERTAINTY

Figure 1.22. Corporate Credit Fundamentals

Default rates have risen somewhat ...

1. Corporate Default Rate

... and some firms are not far from insolvency.

2. Distance to Insolvency in the United States and Europe

(Percent, last 12 months, left scale; basis points, right scale)

(Index)

Bond only actual
Bond only baseline forecast
Loan only actual
Loan only baseline forecast
Global HY spread (right scale)
Global HY spread forecast (right scale)

16

14

12

10

8

6

4

2

0
2006 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

2,000

1,800

1,600

1,400

1,200

1,000

800

600

400

200

Bankruptcy cases continue to increase as smaller firms face diﬃculties.

3. Growth Rate of Corporate Bankruptcies

(Percent, four-quarter change)

United States

Euro area

Japan

50

30

10

−10

−30

−50

2019:Q1

20:Q3

22:Q1

23:Q3

Interquartile range

Median

Highly vulnerable

2017

18

19

20

21

22

23

24

Corporate-debt-to-GDP ratios, adjusted for the maturity of debt, are at
their highest levels since the global financial crisis.

4. Global Nonfinancial-Corporate-Debt-to-GDP Ratios and Average

Duration of Outstanding Debt
(Years; percent, right scale)

Years to maturity (years)
Debt-to-adjusted-GDP ratio (percent)

Debt-to-GDP ratio (percent, right scale)

11

13

15

17

19

21

23

12

11

10

9

8

7
2009

4.0

3.5

3.0

2.5

2.0

1.5

1.0

0.5

0

130

120

110

100

90

80

70

Sources: Bloomberg Finance L.P.; EUROPACE AG/Haver Analytics; JPMorgan; Moody’s; and IMF staﬀ calculations.

Note: Panel 2 shows distance to insolvency, based on Atkeson, Eisfeldt, and Weill (2013), for constituents of the S&P 500 and STOXX Europe 600. In panel 3, the last data
point is 2024:Q2. HY = high yield.

hedging (Figure 1.21, panel 2).26 Other credit instru-
ments have also benefited from buoyant investor
sentiment, as banks’ syndicated lending has regained
some market share from private credit lenders (Figure
1.21, panel 3; Figure 1.24, panel 2; April 2024 Global
Financial Stability Report), and collateralized loan
obligations have experienced their largest issuance27
since the start of the Federal Reserve hiking cycle, as
investors have sought alternative credit products.

Although solid economic activity and healthy corpo-
rate balance sheets with large cash buffers28 have kept
margins robust for some firms, loan and bond default
have steadily risen as weaker firms have struggled
(Figure 1.22, panel 1). Forward-looking metrics like
the global distance to insolvency29 indicate that around
one-quarter of firms are vulnerable to insolvency
(Figure 1.22, panel 2). Bankruptcies among smaller
firms have continuously risen in recent months, with

26Japanese investors typically see US investment-grade corporate
bonds as alternative to high-quality duration products such as Japa-
nese government bonds or US Treasury bonds, although it does not
mean that they do not manage credit risks.

27Collateralized loan obligation issuance volumes in the United
States and euro area for the second quarter of 2024 were 60–90 per-
cent higher than the average volumes between the first quarter of
2022 and the first quarter of 2024.

28On margins for firms, see the October 2023 Global Financial

Stability Report.

29“Distance to insolvency” is a measure, based on Atkeson,
Eisfeldt, and Weill (2013), that aims to measure the financial
soundness of individual firms using data from financial statements
and market-based information. US data include those for constituent
firms of the S&P 500 stock index, and data for Europe include those
of STOXX Europe 600 constituents.

International Monetary Fund | October 2024

27

GLOBAL FINANCIAL STABILITY REPORT: STEAdYING ThE COuRSE: uNCERTAINTY, ARTIFICIAL INTELLIGENCE, ANd FINANCIAL STABILITY

cases exceeding prepandemic levels in Europe and
Japan (Figure 1.22, panel 3). Among investment-grade
firms, the amount of debts issued by “fallen angels”—
issuers that have been downgraded to below invest-
ment grade—is now roughly equal to the amount of
“rising stars”—debt upgraded to investment grade30—
whereas up until the April 2024 Global Financial
Stability Report, rising stars outnumbered fallen angels.
A decrease in average maturity has characterized
the recent corporate debt market, as borrowers have
issued less long-term debt, on average. Although this
is in part a response to a monetary policy cycle with
rate cuts expected in the near future, it has increased
refinancing risks, as repayment obligations are con-
centrated over a short period, all else equal. Indeed, a
version of the debt-to-GDP ratio that accounts for the
remaining years of corporate debt31 shows that corpo-
rate leverage has been rising and is now at about its
highest levels since the period after the global financial
crisis, even though the simple debt-to-GDP ratio has
been declining (Figure 1.22, panel 4). Larger effective
debt burdens could raise concerns relating to financial
stability. More important, this risk is more pronounced
in the high-yield segment, in which the maturity of
debt has dropped much more steeply.32

A deeper look at individual firms reveals that cash
buffers are dwindling, especially for a weak tail of com-
panies. The share of firms with cash-to-interest-expense
ratios below 1.5 has been increasing (Figure 1.23,
panel 1; see also the April 2024 Global Financial Stabil-
ity Report), especially among smaller firms. Worryingly,

30The gap between the market return of a global investment-grade

corporate debt index and the return implied by changes in yields
(adjusted for duration) and coupons has been declining, on a
12-month rolling basis, since mid-2023, while remaining positive.
This implies that the value of the index has declined at a faster pace
than the yields and coupons, primarily because of an increase in the
value of debt of fallen angels (which exit the index as a result of the
downgrades in their ratings).

31A stock-to-stock approach is used here, wherein the flow

variable of GDP is converted into a stock variable by interacting it
with the remaining years to maturity of debt. This allows repayment
obligations to be compared with the resources or earnings generated
over the repayment period. The leverage of global nonfinancial
corporations, measured in terms of the debt-to-GDP ratio under the
stock-to-flow approach, has decreased from 108 percent in 2021 to
below 100 percent currently, as recovery in GDP masks an increase
in nominal debt value. However, the remaining years to maturity of
debt are shortening at a faster pace. Hence, the leverage metrics that
adjust for duration, that is, the debt-to-GDP ratio times the average
remaining life of debts, have been increasing steadily.

32The average remaining life of high-yield debt has declined at
a faster rate, reaching 4.6 years in the fourth quarter of 2023 from
6.7 years in the third quarter of 2009, when leverage based on the
stock-to-stock approach was near the levels seen recently.

28

International Monetary Fund | October 2024

the share of weak small and medium firms has steadily
become larger in advanced economies. Earnings relative
to interest expenses, or the interest coverage ratio (ICR),
have clearly deteriorated over the past year for some
European and emerging Asian countries, including those
that already have lower ICRs (Figure 1.23, panel 2).

For weaker firms, difficulties in servicing debt may be
exacerbated by tough refinancing conditions. Refinancing
yields are significantly higher than coupons on existing
debt, particularly those on debt issued at very low fixed
rates. Among global corporate debt coming due in 2025,
for example, fixed-rate debt accounts for close to 50 per-
cent, with existing coupons between 3.5 and 4 percent,
significantly lower than the current refinancing yield of
5.5 percent (Figure 1.23, panel 3). Should monetary
policy ease, global refinancing would become less costly,
albeit not necessarily cheaper. If that does not happen,
however, refinancing 2024 and 2025 bonds at higher
interest rates would bring ICRs down by an average of
12 percent, reducing debt servicing capacity further.
Refinancing costs remain elevated specifically for
emerging market corporations, especially those for for-
eign currency bonds, putting pressure on debt sustain-
ability (Figure 1.23, panel 4). Correspondingly, issuance
has remained much slower than before the current
monetary tightening cycle (Figure 1.23, panel 5). An
easing in monetary policy would help firms in emerging
markets with their debt sustainability, as would a shift
toward issuing in local, rather than foreign, currencies.
Trade restrictions or geopolitical events would also
likely affect firms, through higher input costs.33 The
resulting margin compression would further deterio-
rate ICRs—in a scenario in which input costs increase
by 10 percent, the weak tail of firms, with ICRs that
are less than one, would increase by an additional
3 to 6 percentage points, depending on the region,
with the impact especially large in emerging markets
(Figure 1.23, panel 6). Although regional or indus-
try heterogeneity are not taken into account in this

33This part of the scenario being presented is calibrated to reflect
higher marginal financing costs (by 150 basis points) and potential
upward pressures on input costs because of factors like recalibration
of international trade policies globally or supply chain disruptions
caused by geopolitical events. These are seen as potentially compress-
ing corporate margins in the near term, although such a compression
would be contingent on the degree of market power (that is, a firm’s
ability to pass on the increase in costs to customers). In other words,
the larger a firm’s market power, the smaller the impact on margins.
Broadly speaking, about 90 percent of firms in advanced economies
have little or no meaningful market power (April 2019 World Eco-
nomic Outlook). Hence, higher input costs will likely affect the profit
margins of these firms adversely.

CHAPTER 1

STEAdYING ThE COuRSE: FINANCIAL MARkETS NAvIGATE uNCERTAINTY

International Monetary Fund | October 2024

29

LargeSmall and mediumAEs AmericasAEs AsiaAEs EuropeEMs AmericasEMs AsiaEMs EuropeAAA to ABBBBB or belowLATAMChinaCEEMEAAsia excl. ChinaPretightening trendline2019:Q42023:Q4ScenarioDebt serviceabilitydeteriorated in 2023:Q4Debt serviceabilityimproved in 2023:Q420242025202620272028Current average refinancing yieldFigure 1.23. Corporate Debt SustainabilityThe shares of firms with less cash buﬀers are increasing.1. Share of Debt Issued by Firms with Cash Buﬀers to Interest ExpenseRatio below 1.5(Percent of total debt, average across countries)050204010300624135ICR has declined faster for European firms, although from high levels. ICR has slipped below two for some emerging Asia countries.2. Interest Coverage Ratios Across Regions and over Time(Ratios)Firms have to refinance 30 percent of existing debts at higher funding costs ...3. Average Coupon of Maturing Debt versus Share of Fixed-Rate Debt(Percent)... and the average borrowing cost for EM corporates has increased over the past two years.4. Average Coupons for Emerging Market Corporate Bonds in DiﬀerentRating Categories(Percent)0102468Sources: Bloomberg Finance L.P.; Bond Radar; Dealogic; IMF, World Economic Outlook database; S&P Capital IQ Pro; and IMF staﬀ calculations.Note: Panels 1, 2, and 6 are based on data from a sample of 19 countries comprising Brazil, Chile, China, France, Germany, India, Indonesia, Italy, Japan, Korea, Malaysia, Mexico, Poland, Russia, Spain, Thailand, Türkiye, the United Kingdom, and the United States. For these panels, the aggregate values represent the average weighted by outstanding debt. In panel 2, the outliers beyond the scale are not displayed in order to improve presentation. In panel 3, the size of the bubbles represents the total outstanding debt maturing. In panel 5, “pretightening trendline” is an average of values for 2019:Q1 and 2021:Q4 at $82.5 billion. In panel 6, “scenario” shows the share of debt that bears an interest coverage ratio of below 1 in the scenario explained in note 37. AEs = advanced economies; CEEMEA = Central and Eastern Europe, the Middle East, and Africa; EMs = emerging markets; excl. = excluding; ICR = interest coverage ratio; LATAM = Latin America.Share of fixed-rate debt in annual maturing debtMedian ICR, 2022:Q4EM hard-currency corporate bond issuance has slowed.5. Emerging Market Hard-Currency Bond Issuance by Region(Billions of US dollars) In a scenario where more trade tensions lead to higher input costs, the share of firms with low interest coverage rises meaningfully above prepandemic levels.6. Share of Corporate Debt with ICR Below One in Total(Percent)2019:Q422:Q423:Q42019:Q422:Q423:Q42019:Q422:Q423:Q42019:Q422:Q423:Q4AEsAEs excl.JapanEMs excl.ChinaEMs excl.China and India01.02.03.04.05.06.0Average coupon onmaturing fixed-rate debt3.03.54.04.55.05.5AEsAEs excl.JapanEMsEMs excl.China10203040506001020304050Median ICR, 2023:Q40204060801001202019:Q119:Q320:Q120:Q321:Q121:Q322:Q222:Q423:Q123:Q324:Q124:Q32019:Q119:Q320:Q120:Q321:Q121:Q322:Q222:Q423:Q123:Q324:Q124:Q3GLOBAL FINANCIAL STABILITY REPORT: STEAdYING ThE COuRSE: uNCERTAINTY, ARTIFICIAL INTELLIGENCE, ANd FINANCIAL STABILITY

Figure 1.24. Expansion in Private Credit Despite Borrowers’ Struggles

Favorable expectations for the private credit industry have supported
stock prices of specialized asset managers.

Private credit expansion beyond middle-market firms intensifies
competition with banks on large deals.

1. Stock Prices of Selected Asset Managers Versus the Rest of the

2. Number of LBOs Financed Through Broadly Syndicated Loans versus

Equity Market
(Normalized, March 2023 = 100)

280
260
240
220
200
180
160
140
120
100
80
Mar. 2023

Ares Management
KKR & Co.
Apollo Global Management
Magnificent 7
S&P 500
KBW Bank Index

Jun. 23

Sep. 23

Dec. 23

Mar. 24

Jun. 24

150

100

50

0

Private Credit
(Number of LBOs; percent)

Private credit

Syndicated
Private credit
(right scale,
percent)

3
Q
:
0
2

4
Q
:
0
2

1
Q
:
1
2

2
Q
:
1
2

3
Q
:
1
2

4
Q
:
1
2

1
Q
:
2
2

2
Q
:
2
2

3
Q
:
2
2

4
Q
:
2
2

1
Q
:
3
2

2
Q
:
3
2

3
Q
:
3
2

4
Q
:
3
2

1
Q
:
4
2

2
Q
:
4
2

2
Q
:
0
2
0
2

High interest rates and leverage have jeopardized borrowers’ ability to
service their debt ...

... and are exerting significant pressure on cash flows of private credit
borrowers.

3. Interest Coverage Ratio and Leverage (Ratio of Debt to EBITDA) for

4. PIK Income as Share of Interest and Dividend Income of US BDCs

Borrowers from US BDCs
(Ratios)

(Percent)

3.0

2.7

2.4

2.1

1.8

ICR (left scale)
Debt/EBITDA (right scale)

1.5
Jun. 2020

Mar. 21

Dec. 21

Sep. 22

Jun. 23

Mar. 24

6.0

5.5

5.0

4.5

4.0

3.5

3.0

2018

19

20

21

22

23

24:Q1

100

80

60

40

20

0

9
8

7

6
5

4

3

2

1

0

Sources: BDC disclosures; Bloomberg Finance L.P.; Fitch; PitchBook; and IMF staﬀ calculations.

Note: Panel 2 is based on US data. In panel 4, when interest is paid in kind, no cash flow occurs. Instead, the interest coupon is added—usually at an extra cost—to the loan’s
principal. BDC = business development company; EBITDA = earnings before interest, taxes, depreciation, and amortization; ICR = interest coverage ratio; LBO =
leveraged buyout; PIK = payment in kind.

scenario, negative fallout could be more pronounced in
trade-dependent economies or industries. In contrast,
some economies or industries could benefit from sup-
ply chain reorganization or trade reallocation.

Private Credit Continues to Grow and
Vulnerabilities Are Rising

Private credit—that is, credit provided outside
the realms of either commercial banks or public
debt markets—continues to grow, and the favorable
outlook for this market has pushed up the stock prices
of specialized asset managers, which have outper-
formed bank stocks and the broader equity market

30

International Monetary Fund | October 2024

(Figure 1.24, panel 1). Private credit has now entered
credit segments beyond lending to midsized corporate
borrowers, intensifying competition with banks in
the syndicated loan markets in which they dominate
(Figure 1.24, panel 2).

However, signs are mounting that high interest
rates are pressing private credit borrowers, and a severe
downturn has not yet tested the many features designed
to mitigate credit risks at the private credit industry’s
current size and scope. There are signs that the private
credit industry’s rapid growth, competition from banks
on large deals, and pressure to deploy capital may be
leading to a deterioration of underwriting standards
and weakened covenants, amid interest rate pressure.

CHAPTER 1

STEAdYING ThE COuRSE: FINANCIAL MARkETS NAvIGATE uNCERTAINTY

Business development companies are often used as a
proxy for the overall industry, as their granular reporting
provides a valuable window on the normally opaque
world of private credit. They show that ICRs have con-
tinued to decline because of borrowers’ high leverage,
the floating rate nature of loans, and the slowdown of
economic activity (Figure 1.24, panel 3). And although
defaults, narrowly defined (that is, missed payments), are
relatively rare among private credit borrowers because
of the inherent flexibility of private credit vehicles to
amend and extend loans (and potentially complement
them with equity warrants; see McDonnell 2024),
default under broader measures, including restructurings
or breaches of covenants, is becoming frequent (Ber-
lin 2024). Indeed, a significant share of borrowers are
facing cash flow pressures, as the ever-growing share of
payment-in-kind coupons shows (Figure 1.24, panel 4).
The opaqueness of the private credit industry makes

it challenging to assess risks related to it and quantify
the full extent of deterioration of private credit loans
(Ellias and de Fontenay 2024; Chapter 2 of the April
2024 Global Financial Stability Report). In a down-
side scenario, stale and uncertain valuations of private
credit could lead to deferred realization of losses
followed by a spike in defaults (April 2024 Global
Financial Stability Report). This possibility makes the
private credit industry vulnerable to episodes of crisis
of confidence, which may be triggered, for example, by
an outsized share of defaults in a group of funds. An
adverse feedback loop could ensue, wherein fundrais-
ing for private credit might be temporarily frozen,
semiliquid funds might suffer runs, and at the same
time, banks or other investors might refuse to continue
providing leverage and liquidity to private credit funds.
Such a scenario could force the entire network of insti-
tutions that participate in the private credit industry to
reduce exposures to the sector simultaneously, trigger-
ing spillovers to other markets and the broad economy.

Real Estate
Home Prices Continued to Decline at a Modest
Pace with Stability Risks Remaining Contained

The decline in global real house prices has contin-
ued at an increasingly modest pace but will be unlikely
to affect the financial stability of households, given
manageable debt burdens and the presence of only
a limited number of complex financial instruments
that can amplify a housing downturn into a broader
turmoil. On an annual basis, real home prices in

emerging markets have declined by 1.6 percent, and in
advanced economies, the drop has been 0.3 percent.
Still, global real house prices remain 5 percent above
the prepandemic average, causing affordability to
remain stretched globally (Figure 1.25, panel 1). Sup-
ply-side constraints, such as rising construction costs
and shortages of construction materials, have partly
dampened the pass-through of elevated interest rates
on demand through lower affordability, particularly in
some countries, resulting in a varying price elasticity of
new housing supply (Figure 1.25, panel 2).

Year-over-year real home price changes are widely dis-
tributed (Figure 1.25, panel 3). Countries with a higher
percentage of variable-rate mortgages, such as Norway
(see also Chapter 2 of the April 2024 World Economic
Outlook) as well as countries experiencing a very large
price buildup in the aftermath of the pandemic (for
example, Canada), have continued to record significant
declines. Home prices in Korea, South Africa, and
Sweden, and, to a lesser extent, the euro area and the
United Kingdom, have also undergone annual declines,
and weak demand has continued to weigh down China’s
property despite recent government support measures
(see “Slowing Growth and Deflationary Pressures Weigh
on China’s Financial System”). The sharp decline in
residential investment observed in some countries,
however, suggests that the house price drop in those
jurisdictions may not extend much further, especially
as supply constraints continue to bind, with price being
further supported by potential improvements in demand
going forward. US house prices, on the other hand, have
increased 2 percent year over year, as housing inventories
have continued to be absorbed briskly and lower mort-
gage rates have boosted refinancing activity and mort-
gage origination. There is still room for house prices to
decline in some jurisdictions, particularly those with
high levels of household leverage (Figure 1.25, panel
4) and overvalued property markets, as well as those
in which substantial easing in monetary policy is less
likely. However, risks to financial stability are contained:
Further increases in mortgage rates are not projected
to raise household debt-servicing expenses significantly
(“Scenario 1” in Figure 1.25, panel 4), a limited number
of risky and complex financial instruments are tied to
the housing market, and household and bank balance
sheets are sound overall.34

34Of issuances, small amounts remain of the private-label residen-
tial mortgage-backed securities that played a role during the global
financial crisis. See Sifma (2024).

International Monetary Fund | October 2024

31

GLOBAL FINANCIAL STABILITY REPORT: STEAdYING ThE COuRSE: uNCERTAINTY, ARTIFICIAL INTELLIGENCE, ANd FINANCIAL STABILITY

Figure 1.25. Developments in Global Residential Real Estate Markets

House prices continue to decline globally, however, at an increasingly
modest pace, with Asia, the euro area, and other advanced economies
leading the decreases.

1. Real Growth in House Prices by Region

(Percent)

Price elasticity of new housing supply continues to show significant
variation among countries.

2. Changes in Real House Prices and Residential Investment

(Proxy for Supply) since 2022
(Percent)

25

20

15

10

5

0

−5

−10

Since before pandemic

Latest (year on year)

North
America

Latin
America

Asia

Euro
area

EU
non-euro
area

Other
advanced
economies

Middle
East and
Africa

Greece

Iceland

United States

Estonia

Finland
Sweden

Canada

New Zealand
Germany

−40

−20

0

20

40

60

Change in residential investment

20

15

10

5

0

−5

−10

−15

−20

−25

s
e
c
i
r
p
e
s
u
o
h

l
a
e
r
n

i
e
g
n
a
h
C

Price change has slowed, with less than 5 percent of jurisdictions
recording double-digit declines.

Contained household debt limits debt-servicing ratios even at higher
mortgage interest rates.

3. Distribution of Yearly Changes in Real House Prices Across Countries

4. Eﬀect of Mortgage Rates on Households’ Debt-Service Ratios

60

50

40

30

20

10

0

(Percent of jurisdictions)

Advanced economies
Emerging market economies

Less than
−10%

−10% to 5% −5% to 0% 0% to 5% 5% to 10%

Greater
than 10%

30

20

10

0

(Percent)

Debt-service ratio in 2024:Q1
Change in reference mortgage rate
(right scale, yearly)

Interest rate increase

a
i
l
a
r
t
s
u
A

m
u
g

i

l
e
B

a
d
a
n
a
C

k
r
a
m
n
e
D

d
n
a
l

n

i
F

e
c
n
a
r
F

y
n
a
m
r
e
G

y
l
a
t
I

n
a
p
a
J

y
a
w

r
o
N

l
a
g
u
t
r
o
P

n
i
a
p
S

n
e
d
e
w
S

s
d
n
a
l
r
e
h
t
e
N
e
h
T

s
e
t
a
t
S
d
e
t
i

n
U

m
o
d
g
n

i

K
d
e
t
i

n
U

1.75

1.25

0.75

0.25

−0.25

Sources: Bank for International Settlements; EUROPACE AG/Haver Analytics (Group of Ten accounts); Federal Reserve Bank of New York Consumer Credit Panel/Equifax;
National Association of Realtors; Organisation for Economic Co-operation and Development; and IMF staﬀ calculations.

Note: In panel 1, “Since before pandemic” refers to 2019:Q4. Countries are grouped based on 2023 real GDP in purchasing power parity from the IMF’s World Economic
Outlook database. “Other advanced economies” includes Australia, New Zealand, Norway, Switzerland, and the United Kingdom. In panel 2, “change in residential
investment” refers to gross fixed capital formation in housing. In panel 4, the debt-service ratio is defined as the ratio of interest payments plus amortizations to income,
assuming debt is repaid in equal portions over the maturity of the loan (that is, no prepayments). The reference mortgage rate in each country is obtained from the Group
of Ten accounts of EUROPACE AG/Haver Analytics. For Belgium, Denmark, Finland, France, Germany, Italy, The Netherlands, Portugal, Spain, Sweden, and the United
Kingdom, the reference mortgage rate is represented by a weighted average of the prevailing mortgage interest rates. For Canada, the reference mortgage rate is the
five-year average residential mortgage lending rate, and for the United States, it is the 30-year fixed mortgage rate. Scenario 1 considers a change in household
debt-service ratios in proportion to the observed or predicted (based on the average mortgage rate change since 2023) change in mortgage interest rates over the
following quarter, with credit obtained by extrapolating the previous year-on-year growth.

Pressures on the Commercial Real Estate Sector
Remain Acute

CRE is at risk of further correction, especially if
financial institutions active in lending to this market
come under strains, including real estate invest-
ment trusts, commercial mortgage-backed securities
(CMBSs), and some banks. Funding could then
be withdrawn discreetly, pushing down prices and

putting more institutions under pressure in an adverse
feedback loop. Based on latest available data, global
CRE prices have fallen by 12 percent year over year,
weighed down by still-high interest rates and poor
investor sentiment (Figure 1.26, panel 1). The US
(European) office sector is experiencing a 23 (16) per-
cent decline. There are signs of stabilization as price
decline of CRE owned by institutional investors has

32

International Monetary Fund | October 2024

CHAPTER 1

STEAdYING ThE COuRSE: FINANCIAL MARkETS NAvIGATE uNCERTAINTY

Figure 1.26. Developments in Commercial Real Estate Markets

The global CRE market continues to reprice ...

1. Changes in Private CRE Valuations
(Percent year over year, 2023:Q4)

10

0

−10

−20

−30

Nominal

Real

l
l

A

l
i
a
t
e
R

e
c
ﬃ
O

l
a
i
r
t
s
u
d
n

I

l
a
i
t
n
e
d

i
s
e
R

l
l

A

l
i
a
t
e
R

e
c
ﬃ
O

l
a
i
r
t
s
u
d
n

I

l
a
i
t
n
e
d

i
s
e
R

l
l

A

l
i
a
t
e
R

e
c
ﬃ
O

l
a
i
r
t
s
u
d
n

I

l
a
i
t
n
e
d

i
s
e
R

l
l

A

l
i
a
t
e
R

e
c
ﬃ
O

l
a
i
r
t
s
u
d
n

I

l
a
i
t
n
e
d

i
s
e
R

Global

Europe

North America

Asia-Pacific

... although demand for CRE diﬀers across regions.

2. Vacancy and Net Absorption Rates Across Cities

(Percent)

Seoul
Tokyo
Berlin
Amsterdam
Singapore
London
Paris
Atlanta
Beijing
Stockholm
New York
Sydney
Washington, DC
Dallas
San Francisco

0

Vacancy

50

−5

0
Net absorption

5

The oﬃce sector continues to face financing challenges.

Significant volumes of CRE debt will mature in the next few years.

3. Cross-Border Investments in Oﬃce

(Billions of US dollars, left axis; share of total investment in sector, percent,
right scale)

4. CRE Maturing Debt and Funding Gap

(Billions of US dollars)

Oﬃce CBD cross-border vol.
Oﬃce sub. cross-border vol.

Oﬃce CBD cross-border share (right scale)
Oﬃce sub. cross-border share (right scale)
50

40

30

20

10

0

08

10

11

13

14

16

17

19

20

22

23

120

90

60

30

0
2007

1,600
1,400
1,200
1,000
800
600
400
200
0

US
Europe
APAC

Oﬃce
Retail
Industrial
Multifamily

2024

25

26

27

28

USA

Europe

Funding conditions are also aﬀecting alternative investors in CRE markets.

CRE markets continue to face downside risks.

5. Expected Default Frequency of REITs

(Percent, one year ahead)

6. Downside Risk to CRE

(Probability density, cumulative real price growth over three years)

8

3

−2

2000

US
Canada
Australia
Europe

North America
Europe
Asia

5th percentile

−19%

−21%

−13%

03

07

10

13

17

20

23

−50 −44 −38 −32 −26 −20 −14 −8 −2

4

10

16

22 28

CRE price growth (percent)

400

300

200

100

0

p
a
g
g
n
d
n
u
f

i

t
b
e
D

0.05

0.04

0.03

0.02

y
t
i
s
n
e
D

0.01

0

Sources: AEW; Bloomberg Finance L.P.; EUROPACE AG/Haver Analytics; Green Street; JLL; MSCI Real Estate; RICS; and IMF staﬀ computations.

Note: Panel 1 shows changes in the asset value growth index across diﬀerent regions and CRE sectors. In panel 2, the net absorption rate indicates the proportion of total
available space that has been absorbed (that is, leased or occupied) within a specific period, relative to the total inventory or supply of space in the market. In panel 3, the
volume of cross-border acquisitions by property type is based on trailing four-quarter data. In panel 4, the debt funding gap for each origination year and sector is based on
the fraction of loans maturing within five years divided by the average loan-to-value ratio for that year. This value is adjusted for expected price corrections and compared
with agencies’ forecast loan-to-value ratios to calculate the debt funding gap with respect to the original loan amount. In panel 6, probability densities are estimated for the
distributions of three-year-ahead (cumulative) CRE price growth (in real terms), following the approach in Deghi, Mok, and Tsuruga (2021). Bullets indicate price declines
with a 5 percent probability. APAC = Asia and Pacific; CBD = central business district; CRE = commercial real estate; REIT = real estate investment trust; sub. = suburban;
vol. = volatility.

International Monetary Fund | October 2024

33

GLOBAL FINANCIAL STABILITY REPORT: STEAdYING ThE COuRSE: uNCERTAINTY, ARTIFICIAL INTELLIGENCE, ANd FINANCIAL STABILITY

slowed, and the spread of prime property yields over
long-term government bond yields has eased in some
regions. Nonetheless, transaction volumes were just
over $130 billion in 2023, a 37 percent decrease from
the previous year.35

Changing international trade patterns, along with

region-specific shocks and postpandemic shifts to
remote working, are leading to diverging country and
regional performance. US metro areas have higher
vacancy rates than those anchored by other global
cities and are projected to have negative net absorption
rates, indicating that occupancy is outpaced by newly
vacant space (Figure 1.26, panel 2). By contrast, tech-
nological transformations like artificial intelligence and
cloud computing are expected to boost demand for
data centers and other similar types of CRE, especially
in Asia-Pacific.

Over the past few years, sources of CRE funding

have shifted significantly. Tight bank lending stan-
dards and subdued investor sentiment are expected
to further restrict CRE financing, leading to project
delays or cancellations and reducing supply.36 Equity
investments by institutional investors have declined
significantly as they favor debt instead.37 Cross-border
investment flows into global property markets, espe-
cially the office sector, would likely remain subdued
in the near term as properties face high vacancy
rates. Historically, offices accounted for 40 percent of
cross-border CRE investments between 2010 and 2023
(Figure 1.26, panel 3). With the rise of hybrid work
models, this share has declined by close to 10 percent-
age points since 2022.

35In Europe, for instance, the excess spread of prime property
yields over long-term government bond yields is rebounding and
nearing its 25-year historical average. Meanwhile, in the United
States, market agencies project rates of capitalization—the ratio of a
property’s net operating income to its value—will peak in 2024.
36Total cumulative distress related to US commercial property
reached $94.2 billion in the second quarter of 2024, with $10.6 bil-
lion of new distress in the period. Spikes in the numbers of termi-
nated deals (when a property goes under offer and the transaction
collapses) and of pulled offers (when assets are brought to market
but do not sell) also indicate the dislocation. Globally, the number
of such events spiked in the first quarter of 2024 to the highest levels
since 2010.

37Debt funds have significantly outperformed equity investments
in European real estate since the end of 2022, according to MSCI’s
recently launched Europe Quarterly Private Real Estate Debt Fund
Index, as higher rates have led to a widespread correction in property
values. The presence of CRE debt premiums, as indicated by the
spread between 10-year fixed-rate CRE and corporate A to Baa rates,
could be driving this correction.

34

International Monetary Fund | October 2024

In the United States, banks with global footprints

have the greatest exposure to vulnerable loans on
central business district offices, with this segment
accounting for 26 percent of their total CRE loan
originations over the past three years, whereas the
same share is just 4 percent for national and regional
and local banks. Banks could lend more conser-
vatively toward central business district office and
other vulnerable CRE segments, posing challenges
to refinancing of a high volume of loans coming due
(Figure 1.26, panel 4). In the United States alone,
nearly $1 trillion in CRE debt will mature between
2024 and 2025, with a funding gap of almost $300
billion. Globally, about 40 percent of loans held by
banks, 25 percent by commercial mortgage-backed
securities, and 20 percent by investor-driven lenders
like debt funds are maturing over this period. CMBS
lenders have the largest exposure to loans maturing
in 2024, accounting for nearly 30 percent of the
balance. Strains in the sector are likely to persist, as
delinquencies of CMBSs specializing in office proper-
ties are above 8 percent, up 3 percentage points from
the previous year, and CMBSs still have very wide
spreads. Real estate investment trusts, which depend
on bank funding for liquidity, have elevated expected
frequencies of default in Canada and the United
States (Figure 1.26, panel 5).

Overall, the unprecedented combination of
maturing debt, high interest rates, general dearth
of CRE sales, and varied effects across property
types distinguish this CRE cycle from past ones.
Rate cuts alone might not resolve all the challenges
facing investors in CRE, as many markets con-
tinue to contend with postpandemic remote work
that has reshaped CRE demand, particularly in the
market for central business district offices. Taking
into account various supply, demand, and financ-
ing factors, the CRE price-at-risk model of Deghi,
Mok, and Tsurunga (2021) indicates that CRE
prices still have room to correct. With 5 percent
probability, real prices are estimated to decline over
the next three years by about 20 percent in North
America and 19 percent in Europe (Figure 1.26,
panel 6).38

38CRE price projections in an adverse scenario with 5 percent
probability are based on a CRE prices-at-risk model. For further
details, see Deghi, Mok, and Tsuruga (2021). The analysis suggests
that prolonged high interest rates and tighter financing conditions
heighten downside risks to CRE.

CHAPTER 1

Steadying the CourSe: FinanCial MarketS navigate unCertainty

Figure 1.27. Bank Exposures in Commercial Real Estate Offices

A high share of banks has concentrations in CRE
office exposures.

1. Commercial Real Estate and

Office Concentrations
(Percent)

600

500

400

300

200

100

0

CRE to Tier 1 capital
CRE oﬃce to Tier 1
capital (right scale)

United States

Europe Asia

300

250

200

150

100

50

0

Most banks in Europe and the United States
can withstand a severe CRE office shock, but
some might face solvency challenges.

Banks with more detailed CRE disclosure
outperform banks without office disclosures.

2. Tier 1 Capital Ratios Before and After

3. One-Year Changes in Banks’ Stock Prices

Severe Stress
(Percent)
Tier 1 capital ratio
Tier 1 capital ratio after severe stress

United States

Europe

20

16

12

8

4

0

(Median percent)

80

40

20

0

−20

−40

High CRE
concentration with
CRE office disclosure

High CRE
concentration without
CRE office disclosure

Sources: Bloomberg Finance L.P.; S&P Capital IQ Pro; US firms’ annual reports and Securities and Exchange Commission Forms 10-Q and 10-K; and IMF staff estimates.

Note: In panel 1, dashed lines indicate high concentrations of CRE offices, defined as ratios of exposures of CRE offices to Tier 1 capital above 50 percent. In panel 2, high
concentrations of CRE offices are defined as ratios of exposures to CRE offices to Tier 1 capital greater than 50 percent. Severe stress corresponds to a scenario in which
exposures to CRE offices lose 50 percent of their value. In panel 3, “high CRE concentration” is defined as ratios of CRE exposures to Tier 1 capital plus loan loss reserves
greater than 300 percent in the United States and ratios of CRE exposures to Tier 1 capital greater than 100 percent in Europe. Median shown as middle line in each box;
dots depict outliers. CRE = commercial real estate.

Concentrated Exposure in Office Commercial
Real Estate May Challenge Some Banks

Pressures on CRE have kept banks with large

exposures to it in the spotlight. Although most banks
appear to have adequate loan loss reserves and cap-
ital buffers to absorb potential CRE losses, some,
particularly those with exposures concentrated in the
office segment, might face challenges. A review of the
financial reporting of 398 banks in Asia, Europe, and
the United States, including all global systemically
important banks, reveals that many have a high ratio
of CRE loans to Tier 1 capital, particularly in the
United States (Figure 1.27, panel 1).39 In this sample,
only about one-quarter of publicly traded US banks
disclose exposures to the embattled office sector, and
only a few European banks disclose this information.40
Nonetheless, among banks that report information on
CRE offices, many have large exposures, with about
25 percent of sample US banks and almost 50 percent
of sample European banks reporting CRE office

39A high ratio is defined here as a CRE exposure in Tier 1 capital
greater than 300 percent in the United States and 100 percent in Europe.
40More German banks opted to disclose CRE exposures in the
second half of 2023 to alleviate investors’ concerns. See IMF (2024a,
p. 22).

exposures in Tier 1 capital greater than 50 percent
(Figure 1.27, panel 1).

In an adverse scenario in which CRE office expo-

sures lose 50 percent of their value, the aggregate
Tier 1 capital ratio of US banks would decrease from
12.3 to 11.3 percent. Among European banks, the
ratio would drop from 17 to 13.3 percent (Figure
1.27, panel 2).41 Although such a shock seems man-
ageable at an aggregate level, 4 percent of the banks in
the sample (US and European banks)—representing
1 percent of assets—would find their Tier 1 capital
ratios dipping below 7 percent. The lack of granular
CRE disclosures complicates risk assessments, and
investors appear to penalize banks that forgo providing
detailed information. For example, stock prices of US
banks with high CRE concentrations that disclose their
office exposures tend to outperform those of banks not
disclosing them (Figure 1.27, panel 3).42

41A simplified severe CRE office stress test was performed for a
sample of 14 banks in Europe and 145 banks in the United States
that disclosed CRE office exposures in their periodic reporting as of
the end of 2023 or the first quarter of 2024.

42As measured by changes in one-year stock prices as of July 31,

2024.

international Monetary Fund | october 2024

35

GLOBAL FINANCIAL STABILITY REPORT: STEAdYING ThE COuRSE: uNCERTAINTY, ARTIFICIAL INTELLIGENCE, ANd FINANCIAL STABILITY

Figure 1.28. Broad Resilience in the Banking Sector, with Persistent Weakness Among Several Small Banks

Modest deterioration of asset quality has helped banks’ earnings.

The relatively favorable outlook has improved valuations.

1. Ratio of Nonperforming Loans to Gross Loans

(Weighted-average ratio, percent)

2. Price-to-Book Multiplier

(Percent)

7

6

5

4

3

2

1

0

8
1
0
2

.
r
a
M

US
Japan
EMs excl. China

Europe
China

US
China

Europe
EMs excl. China

Japan

SVB collapse

8
1

.

p
e
S

9
1

.
r
a
M

9
1

.

p
e
S

0
2

.
r
a
M

0
2

.

p
e
S

1
2

.
r
a
M

1
2

.

p
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2
2

.
r
a
M

2
2

.

p
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3
2

.
r
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3
2

.

p
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S

4
2

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r
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1
2

.
r
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1
2

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1
2

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2
2

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2
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3
2

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1.6

1.4

1.2

1.0

0.8

0.6

0.4

0.2

The number of banks with multiple flags has diminished, signaling
receding risk.

The participation of small banks in the IMF monitoring list is increasing.

3. Total Assets of Banks Signaling Vulnerabilities in Three or More Areas

4. Participation in IMF’s Monitoring List by Banks’ Size

of Risk
(Trillions of US dollars)

(Percent, average number of KRI)

Assets: 4+ flags
Forecast
Num. banks: 4+ flags

Assets: 3 flags
Forecast
Num. banks: 3 flags

Forecast
period

<30 billion
100 billion–1 trillion
Number of KRIs triggered for banks with 4+ flags (right scale)

30–100 billion
>1 trillion

s
t
e
s
s
A

50
45
40
35
30
25
20
15
10
5
0

s
k
n
a
b
f
o
r
e
b
m
u
N

140

120

100

80

60

40

20

0

100

80

60

40

20

0

6.0

5.5

5.0

4.5

4.0

2
Q
:
8
1

3
Q
:
8
1

4
Q
:
8
1

1
Q
:
9
1

2
Q
:
9
1

3
Q
:
9
1

4
Q
:
9
1

1
Q
:
0
2

2
Q
:
0
2

3
Q
:
0
2

4
Q
:
0
2

1
Q
:
1
2

2
Q
:
1
2

3
Q
:
1
2

4
Q
:
1
2

1
Q
:
2
2

2
Q
:
2
2

3
Q
:
2
2

4
Q
:
2
2

1
Q
:
3
2

2
Q
:
3
2

3
Q
:
3
2

4
Q
:
3
2

1
Q
:
4
2

2
Q
:
4
2

3
Q
:
4
2

4
Q
:
4
2

1
Q
:
8
1
0
2

2
Q
:
8
1

3
Q
:
8
1

4
Q
:
8
1

1
Q
:
9
1

2
Q
:
9
1

3
Q
:
9
1

4
Q
:
9
1

1
Q
:
0
2

2
Q
:
0
2

3
Q
:
0
2

4
Q
:
0
2

1
Q
:
1
2

2
Q
:
1
2

3
Q
:
1
2

4
Q
:
1
2

1
Q
:
2
2

2
Q
:
2
2

3
Q
:
2
2

4
Q
:
2
2

1
Q
:
3
2

2
Q
:
3
2

3
Q
:
3
2

4
Q
:
3
2

1
Q
:
4
2

2
Q
:
4
2

3
Q
:
4
2

4
Q
:
4
2

1
Q
:
8
1
0
2

Sources: Bloomberg Finance L.P.; Visible Alpha; and IMF staﬀ calculations.

Note: In panel 3, the forecast period is based on aggregate consensus analyst forecasts that are used to track the evolution of the key risk metrics over the subsequent
three quarters as a measure of forward-looking risks. In panel 4, banks’ size refers to banks’ total assets in US dollars. EMs = emerging markets; excl. = excluding; KRI =
key risk indicator; num. = number of; SVB = Silicon Valley Bank.

Bank and Nonbank Financial
Intermediaries
The Global Banking Sector Is Resilient Although
a Weak Tail of Smaller Banks Faces Challenges

The global banking sector has remained resilient
since the April 2024 Global Financial Stability Report,
with capital and liquidity buffers ample and profitabil-
ity having improved. Although NPL ratios have risen
in some forms of lending, such as consumer credit

cards, auto loans, and CRE, overall asset quality has
not deteriorated significantly (Figure 1.28, panel 1).
Banks’ profitability has benefited from higher nonin-
terest income, like fees and commissions, and from
measures to reduce operational costs, pushing up their
stock valuations (Figure 1.28, panel 2). In the near
term, net interest margin and bank profitability could
be adversely affected by interest rate cuts, as banking
assets tend to reprice more quickly than deposits.
However, in the medium term, lower interest rates

36

International Monetary Fund | October 2024

CHAPTER 1

STEAdYING ThE COuRSE: FINANCIAL MARkETS NAvIGATE uNCERTAINTY

could stimulate a rebound in lending, and reduced
refinancing costs might help alleviate some of the pres-
sures facing the CRE sector.

The IMF staff ’s key risk indicators (Chapter 2 of
the October 2023 Global Financial Stability Report)
capture the improved risk outlook, with fewer banks
expected to be flagged as deficient in three or more
risk indicators by the end of the year (Figure 1.28,
panel 3). However, the number of banks with four or
more weak risk indicators is expected to rise, sug-
gesting that weak banks are becoming increasingly
vulnerable. This trend appears to be more pronounced
in Asia and reflects expectations for a deterioration of
asset, liquidity, and market risk metrics.

Smaller banks with assets less than $100 billion have

featured more prominently on the monitoring list in
recent times (Figure 1.28, panel 4). Although specific
reasons for their weaknesses vary, many face challenges
related to their business models that result in lower
earnings and underperformance or undervaluation of
their stocks. In the United States, unrealized losses in
securities portfolios and high CRE exposures remain
a concern (see “Concentrated Exposure in Office
Commercial Real Estate May Challenge Some Banks”).
Some banks have recently increased their use of syn-
thetic risk transfers to manage risks and boost capital
ratios, which requires attention from supervisors (see
Box 1.1).

Although the bout of market volatility in early
August has led only to a temporary sell-off of some
banks’ stocks, its cause—investor fears about a forth-
coming recession—highlights the challenges facing
the banking industry. An economic slowdown can
deteriorate asset quality and reduce loan demand,
and the associated easing of monetary policy will
likely lower interest income, at least in the short
term. Importantly, during downturns, investors can
shift rapidly from a balance sheet view to a mark-to-
market view of risks, in which they assess a bank’s
viability based on the market value of its assets,
irrespective of their accounting or regulatory value.
Supervisory attention to the effect of a downturn
on banks’ safety and the soundness of their business
models, especially for weak institutions, is para-
mount. The significant risk that financial crimes
pose to macrofinancial stability also requires the
integration of measures against money laundering
and the financing of terrorism within the broader
financial stability framework.

Growth of Bond Funds Renews Concerns About
Maturity Mismatches and Use of Leverage

The potential mismatch between the liquidity
of underlying assets and redemption terms is a key
vulnerability of the asset management industry because
it could precipitate forced selling when asset prices
are falling. There are signs that this vulnerability is
growing among open-ended bond mutual funds. Many
allow for daily redemptions, whereas the underlying
assets are relatively illiquid compared to, for example,
equity funds. Two trends are contributing to the grow-
ing significance of this vulnerability. First, bond funds
have grown strongly over past decades, with assets
under management increasing sevenfold between 2009
and 2024 in the US market. Holdings of US bonds
among exchange-traded funds (ETFs) and open-ended
mutual funds now account for about 25 percent of the
total outstanding, up from about 10 percent in 2009
(Figure 1.29, panel 1). Second, there has been a rota-
tion toward institutional investment funds and ETFs,
with institutional mutual funds having overtaken retail
mutual funds in size.

Bond ETFs and institutional mutual funds are more

likely to face large and sudden outflows, with fund
flows-at-risk that have higher (across funds) medians
and larger ranges (Figure 1.29, panel 2). Although the
structure of ETFs—through authorized participants—
partly shields the underlying bond market from sudden
redemptions, these funds may have a less stable inves-
tor base attracted to intraday trading and shorting of
ETF shares (Cai and others 2024). There is consider-
able heterogeneity across types of ETFs, and some face
large peak outflows, as their large fund flows-at-risk
reflect.43 Peak outflows are also larger for institutional
mutual funds compared with those for retail mutual
funds, possibly because institutional investors are more
active in reallocating investments than retail ones.

Sudden fund outflows could lead to forced sales of
assets funds hold, affecting the broader market. And in
less liquid markets, there may be an adverse feedback
loop: Investors who are aware of the illiquidity of their
funds’ underlying assets may withdraw their invest-
ments more quickly and in larger quantities; these
large outflows would have outsized price effects on

43Fund flows-at-risk are defined as the 5th percentile of the his-
torical flow distribution. The analysis presented in this section uses
monthly data covering 2014–24 to calculate the fund flows-at-risk,
at the level of each individual fund.

International Monetary Fund | October 2024

37

GLOBAL FINANCIAL STABILITY REPORT: STEAdYING ThE COuRSE: uNCERTAINTY, ARTIFICIAL INTELLIGENCE, ANd FINANCIAL STABILITY

Figure 1.29. Growth, Outflows, and Leverage of Bond Funds

US bond funds’ assets under management have grown strongly.

1. US Bond Fund Assets Under Management

(Percent of bonds outstanding, left scale; institutional share of mutual fund
holdings, percent, right scale)

Peak outflows tend to be higher for institutional mutual funds and ETFs.
2. Distribution of Monthly Fund Flows-at-Risk Among US Bond Funds
(Median and interquartile range, percent of assets under management)

Institutional MFs
Retail MFs
Institutional share of MF holdings
(right scale)

ETFs

30

25

20

15

10

5

0
2007 08 09 10 11

12

13

14

15

16

17

18

19

20

21

22

23

24

70

60

50

40

30

20

10

0

16
14
12
10
8
6
4
2
0

Retail

Institutional

MFs

ETFs

Fund flows-at-risk are particularly high for emerging market bond funds,
especially ETFs.

Leveraged bond funds take on significant leverage through repurchase
agreements.

3. Median Fund Flows-at-Risk by Bond Fund Type

(Percent of assets under management)

4. Median and Peak US Bond Fund Repo Usage

(Percent of assets under management)

30

25

20

15

10

5

0

MFs ETFs MFs ETFs MFs ETFs MFs ETFs MFs ETFs MFs ETFs

EM LC
bonds

EM HC
bonds

Government
bonds

HY
corporate
bonds

IG
corporate
bonds

Loans

Leveraged funds can face very large outflows ...

Median

Peak

Median

Peak

Non-leveraged

Leveraged

... but account for a relatively small share of most jurisdictions’ bond
mutual fund universe, with some exceptions.

5. Monthly Fund Flows-at-Risk

(Percent of assets under management)

6. Leveraged Fund Share by Country
(Share of bond funds, percent)

90
80
70
60
50
40
30
20
10
0

ETFs

MFs

ETFs

MFs

Non-leveraged

Leveraged

e
c
n
a
r
F

g
r
u
o
b
m
e
x
u
L

e
r
o
p
a
g
n
i
S

n
e
d
e
w
S

y
n
a
m
r
e
G

s
F
T
E
S
U

d
n
a
l
e
r
I

K
U

l
i
z
a
r
B

S
U

120

100

80

60

40

20

0

8
7
6
5
4
3
2
1
0

Sources: Bloomberg Finance L.P.; EPFR; Lipper; and IMF staﬀ calculations.

Note: “Fund flows-at-risk” are the 5th percentile of flows, based on historical flow data, that is, in 5 percent of cases, the outflows would have been larger. In panel 1, ETF
and MF holdings of US-domiciled funds are compared with the market value of the Bloomberg Barclays US aggregate bond index. Some holdings, however, may be in
foreign bonds, and some US bonds may be held by foreign funds. The percentages shown in panel 1 are therefore indicative. In panels 2–5, the analysis is based on Lipper
data covering US-domiciled bond funds. The panels show median values and interquartile ranges across funds within each category of funds. The median flow, the fund
flows-at-risk, and the median and peak repo usage are first computed for each individual fund, based on monthly data spanning 2014–24, before the distribution across
funds is computed. Peak repo usage refers to the 95th percentile of a fund’s monthly data on repo usage. The analysis in panel 6 is based on Lipper data covering bond
funds and is subject to fund coverage and classification by Lipper. The panel shows a selection of jurisdictions. The share of leveraged US bond ETFs is added for
illustration; for other countries, only the share of leveraged bond mutual funds is shown. In all panels, the analysis of MF flows covers open-ended MFs only, and “MFs” is
used as shorthand for open-ended MFs. EM = emerging market; ETF = exchange-traded fund; HC = hard currency; HY = high yield; IG = investment grade; LC = local
currency; MF = mutual fund.

38

International Monetary Fund | October 2024

CHAPTER 1

STEAdYING ThE COuRSE: FINANCIAL MARkETS NAvIGATE uNCERTAINTY

Figure 1.30. Hedge Funds and Carry Trades

Hedge funds, along with other leveraged
institutions and investment funds, built
substantial short positions in yen futures ...
1. Noncommercial Net Positions on

Yen Futures
(Billions of US dollars)

... and long positions in equity options and
futures ...

... that contributed to market volatility and
generated substantial losses when they were
unwound in early August.

2. Ratio of Long to Short Futures and Options

3. Returns of Commodity Trading Advisor

in US Equities

US equities

15

10

5

0

−5

−10

−15

2019

Asset manager/
institutional
Leveraged funds

Aug. 6,
2024

July 2,
2024

Hedge Funds
(Index)

2.5

2.0

1.5

1.0

0.5

0

115

110

105

100

95

90

Jan. 2023

Jul. 23

Jan. 24

Jul. 24

20

21

22

23

24

2008 10

12

14

16

18

20

22

24

Sources: Bloomberg Finance L.P.; MSCI; US Commodity Futures Trading Commission; and IMF staff calculations.

Note: Panel 1 shows estimated net positions in US dollars based on the number of long and short contracts reported on a weekly basis. In panel 2, “US equities” comprises
futures and options on E-mini contracts for the S&P 500, the Dow Jones, and the Nasdaq for both noncommercial asset managers and leveraged funds. The index in
panel 3 is calculated as the weighted average return of 10 selected mutual funds managed by some of the largest commodity trading advisor hedge funds globally.

the underlying market, further exacerbating illiquidity.
Bond funds in emerging markets—both those in local
and those in hard currency—stand out in this regard,
as they have relatively large fund flows-at-risk (Fig-
ure 1.29, panel 3) and the underlying market is also
relatively illiquid (see Chapter 1 of the April 2024
Global Financial Stability Report).

With investors likely aware of the risk associated
with funds that employ repos (Figure 1.29, panel 4),
leveraged bond funds tend to experience larger peak
outflows compared with their nonleveraged peers
(Figure 1.29, panel 5). The former currently consti-
tutes a small share of the bond fund sector, although
there are differences across jurisdictions (Figure 1.29,
panel 6). Regulators should be aware that deleveraging
by even a small set of funds could have an outsized
effect on the broader financial system (see also Breeden
2022).

Hedge Funds Were Both Catalysts and Victims of
the August Market Sell-Off

The spike in volatility observed in early August
is another example of how leveraged NBFIs, such
as hedge funds—a $7 trillion industry very much
connected to the rest of the financial markets—

can propagate strains through the financial system
and amplify stress (FSB 2023; April 2024 Global
Financial Stability Report). Hedge funds with strate-
gies based on momentum and macroeconomic factors
participate heavily in carry trades, a strategy that
involves borrowing in a country with low interest
rates to invest in other assets or currencies with a
higher return. During the last few years, these hedge
funds contributed to building up substantial short
positions in yen (Figure 1.30, panel 1), which they
often matched with long positions in US equity
futures (Figure 1.30, panel 2) and in currencies of
emerging markets (see Box 1.3). After the Bank of
Japan’s monetary policy decision, worse-than-expected
labor market data in the United States sparked
renewed fears of a recession and rapidly narrowed
the interest rate differential between Japan and the
United States, equities declined, and the yen appre-
ciated. Because of these market moves, many hedge
funds reportedly reached risk limits and received
increased margin calls, which forced them to rapidly
close their positions, erasing the year’s returns for
many hedge funds (Figure 1.30, panel 3). Even in the
absence of hedge fund failures, which could generate
counterparty risk and transmit the shock to bank
and nonbank institutions, the rapid unwinding of

International Monetary Fund | October 2024

39

GLOBAL FINANCIAL STABILITY REPORT: STEAdYING ThE COuRSE: uNCERTAINTY, ARTIFICIAL INTELLIGENCE, ANd FINANCIAL STABILITY

Figure 1.31. Defined-Contribution Pension Funds and Unit-Linked and Annuity Insurers

The shift toward defined-contribution pensions ...

... and unit-linked insurance products has continued, but the rate of growth
and the products’ relative size diﬀer significantly across countries.

1. Share of Defined Contributions in Total Pension Assets by Country

2. Share of Unit-Linked and Annuities in Total Insurance Assets by Country

(Percent)

(Percent)

100

80

60

40

20

0

2017

2023

2017

2023

Australia

United
States

Canada

United
Kingdom

Netherlands,
The

Japan

United
Kingdom

United
States

Italy

Netherlands,
The

France

Germany

100

80

60

40

20

0

Selected defined-contribution pension and superannuation funds are
increasing their illiquid investments ...

... in a manner similar to US insurance annuities, but European unit-linked
products have not seen an increase in their illiquid investments.

3. Share of Illiquid Private Investments in Total Assets

4. Share of Illiquid Investments in Total Assets

(Percent)

(Percent)

14

12

10

8

6

4

2

0

Private equity allocation

Private credit allocation

General account
Unit-linked and annuities

2017

23

Defined-benefit pension funds

2017

23

Selected defined-contribution
pension funds

2017

22

2017

23

US insurers

EU insurers

30

25

20

15

10

5

0

Sources: American Council of Life Insurers; Bank of England; European Insurance and Occupational Pensions Authority; Preqin; Thinking Ahead Institute; and IMF staﬀ
calculations.

Note: The calculations for defined-contribution pensions in panel 1 are based on data from the Thinking Ahead Institute. The calculations in panel 3 are based on the
private equity and private credit investments of a sample consisting of 26 selected defined-contribution private pension and superannuation funds with $1.4 trillion in
assets under management domiciled in Australia, Canada, Germany, Mexico, Sweden, Switzerland, the United Kingdom, and the United States. The defined-benefit
calculations assume all public pension funds in the Preqin database are held in defined-benefit plans. The calculations for illiquid investments in panel 4 do not include
the same items in the European Union and the United States and therefore are not directly comparable. The illiquid investments for US insurers are calculated as the sum
of miscellaneous assets, mortgages, and real estate investments as of the end of 2022, according to the latest published factbook from the American Council of Life
Insurers. The illiquid investments of EU insurers are calculated as the sum of investments in real estate funds, alternative funds, private equity funds, infrastructure funds,
real estate structured notes, real estate collateralized securities, mortgages, loans, and property as defined by the European Insurance and Occupational Pensions
Authority.

crowded and concentrated positions could exacerbate
price movements across global indices, propagating
the stress throughout the financial system. With
limited transparency in the hedge fund industry, it
might also be difficult for investors and supervisory
authorities to gauge how much leverage is still in the
system in real time and what might trigger another
bout of hedge fund deleveraging.

Illiquid Investments by Pensions and Insurance
Raises Maturity Mismatch Vulnerabilities

The share of defined-contribution pensions and
unit-linked insurance products has risen globally in
recent years (Figure 1.31, panels 1 and 2). As cli-
ents holding these products bear any profits and
losses of the underlying investments, providers of
defined-contribution plans typically offer clients

40

International Monetary Fund | October 2024

CHAPTER 1

STEAdYING ThE COuRSE: FINANCIAL MARkETS NAvIGATE uNCERTAINTY

frequent opportunities to enter or exit investment
options. This flexibility may exacerbate liquidity
mismatches between the underlying assets—especially
illiquid assets, such as private equity and credit—and
plan liabilities because the effective duration of the
liabilities has been reduced. For example, Australian
superannuation funds are required to allow clients to
switch between different investment options gener-
ally within three business days, even though these
funds hold, on average, illiquid exposures exceeding
20 percent of their total assets.44 This liquidity mis-
match could affect members’ outcomes in a liquidity
stress event. Furthermore, liquidity stress could spill
over to financial markets, especially those markets
in which pension funds and insurers have a large
footprint, such as government bonds, equities, and
corporate bonds.

There is some evidence that this type of liquid-
ity mismatch is on the rise. Selected large private
defined-contribution pension and superannuation
funds have increased the amount of their assets allo-
cated to illiquid private equity and credit in recent
years (Figure 1.31, panel 3), and several countries have
recently introduced initiatives to encourage further
allocation to illiquid investments.45 In the United
States, annuities (a type of unit-linked insurance
product) have also increased their allocations to illiquid
investments, in a manner similarly to that observed
in general accounts, which already hold a substan-
tially higher proportion of illiquid assets (Figure 1.31,
panel 4). However, European unit-linked insurance
products do not appear to have increased the shares of
illiquid investments in their portfolios, with exposures
to assets such as real estate and private equity limited

44Illiquid level 3 assets in five of the largest Australian superannu-
ation funds, with assets under management exceeding $0.5 trillion,
are estimated to account for almost one-quarter of total assets (Brad-
ley 2023). Note that prudential regulations in Australia require super
funds to determine sufficient liquidity levels within each investment
option to manage client switching.

45The US Department of Labor has provided guidance on how

US plan fiduciaries may offer certain private-asset investments
without violating regulations associated with the Employee Retire-
ment Income Security Act of 1974 (Pensions&Investments 2023).
Former UK Chancellor of the Exchequer Jeremy Hunt has called
on defined-contribution pension schemes to boost investment in
unlisted UK equities (Hunt 2023). The European Union’s Reformed
European Long-Term Investment Fund (ELTIF 2.0) regulation,
which has widened the scope of eligible assets and relaxed diver-
sification and concentration rules, is seen as more friendly toward
investment in illiquid assets (JPMorgan 2024).

and materially smaller than those of European general
account insurers (Figure 1.31, panel 4).

Policy Recommendations

Inflation continues to moderate in many countries,
and markets are pricing in multiple cuts in policy rates
from major central banks. Yet economic uncertainty
is elevated, and adverse surprises to either inflation
or growth could drive financial market reactions that
might complicate central banks’ task. Although mon-
etary policy should always be data dependent, clear
communications from central banks that the path of
policy rates should not react excessively to any indi-
vidual data point would help ameliorate uncertainty
by underpinning their commitment to achieving their
objectives. Where growth and inflation momentum are
set to continue to slow, central banks should gradually
ease monetary policy toward a more neutral stance.
Where inflation remains stubbornly above central
banks’ targets, central banks should push back against
overly optimistic investor expectations for monetary
policy easing.

The reduction of central banks’ balance sheets has
so far unfolded in an orderly fashion. But since more
central banks are now engaging in quantitative tighten-
ing simultaneously, the decline of central bank reserves
is global, requiring careful monitoring of and pre-
paredness for the impact on funding markets. Central
banks should monitor a broad spectrum of indicators
encompassing both liquidity conditions and funding
rates in money markets, and remain attuned to poten-
tial uneven distribution of liquidity and central bank
reserves across banks, while standing ready to address
market stresses. Policymakers should clearly communi-
cate the objectives and steps for removing liquidity.

Many emerging markets have made notable progress

on inflation, but central banks should continue to
ensure inflation targets are met and preserve resilience
against external pressures amid elevated economic
uncertainty. Countries should integrate their policies,
where applicable, using the IMF’s Integrated Policy
Framework. The use of foreign exchange interventions
may be appropriate as conditions warrant and provided
intervention does not impair the credibility of mac-
roeconomic policies or substitute for their necessary
adjustment. In the event of imminent crises, capital
flows management measures may be an option for
some countries as part of a broader policy package to

International Monetary Fund | October 2024

41

GLOBAL FINANCIAL STABILITY REPORT: STEAdYING ThE COuRSE: uNCERTAINTY, ARTIFICIAL INTELLIGENCE, ANd FINANCIAL STABILITY

lessen outflow pressures. Those measures should not
substitute for warranted macroeconomic adjustments
or policies that can help contain systemic risks from
capital flows.

With levels of sovereign debt in many countries sub-
stantially above prepandemic levels, fiscal adjustments
should primarily focus on credibly rebuilding buffers
to keep external financing costs reasonable and to
help anchor medium-term inflation expectations. For
countries with less fiscal space, the credibility of fiscal
plans is imperative to prevent cliff effects in ratings,
which could adversely affect financing conditions.
Countries near debt distress should enhance early
contact with creditors. Bilateral and private sector cred-
itors should find ways to coordinate preemptive and
orderly restructuring to avert costly hard defaults and
prolonged loss of market access. The Group of Twenty
Common Framework should be used when applicable,
and further efforts should be made to improve the
forum’s effectiveness. Continued use of enhanced col-
lective-action clauses in international sovereign bonds
and the development of majority voting provisions
in syndicated loans would help facilitate future debt
restructurings to be preemptive and orderly. Countries
able to access funding should borrow prudently and
avoid excessive debt issuance, which may compro-
mise medium-term sustainability. Moreover, countries
should foster economic growth to create space for
financing development and climate-related spending
while keeping debt on a sustainable path.

To durably improve confidence and alleviate

disinflationary pressures, China still needs accommo-
dative macroeconomic policies along with structural
and promarket reforms to bolster near-term activity,
mitigate risks, and ensure a smooth transition toward
higher-quality and more balanced growth over the
medium term. Property sector policies should prior-
itize the completion of presold unfinished housing
and the restructuring troubled property developers
in a timely manner. Additional easing of monetary
policy, especially through lower interest rates, and
reorientation of public expenditures toward households
could bolster near-term recovery, and comprehensive
fiscal reforms are needed to ensure the sustainability
of local government finances. Policy response should
balance the medium-term health of balance sheets in
the financial sector amid slowing credit growth. For
the banking sector, it is critical to enforce prudential
policies strictly, by phasing out regulatory forbearance

42

International Monetary Fund | October 2024

measures and maintaining adequate loss-absorbing
buffers, among other measures, to strengthen efforts
to restructure weak small- and medium-sized banks
and safeguard risks to financial stability. The Chinese
authorities have made progress in reducing risks in the
nonbank financial sector, but additional regulatory
measures to enhance management of liquidity and
maturity risk, as well as to close regulatory and data
gaps, could help contain future systemic risks.

Climate finance needs to be ramped up, including

adaptation finance. Widespread consensus on the
importance of adaptation has yet to catalyze meaning-
ful private sector participation, as adaptation finance
has so far solely relied on government expenditures.
Creating investment opportunities attractive to private
investors, especially in emerging market and develop-
ing economies, is the key challenge. To scale up adap-
tation finance, it is essential to align the interests and
actions between the public and private sectors, improve
the tracking and measurement of adaptation finance
flows, provide investment guidance and adaptation
taxonomies, and integrate adaptation considerations
across asset classes. Continued support to low-income
and vulnerable middle-income countries is imperative.
Since its establishment in 2022, the Resilience and
Sustainability Trust has been integrating adaptation
support in its 18 programs, with country authorities
emphasizing the importance of building economic
resilience in the face of climate change risks.

Continued vigilance is warranted to monitor vul-
nerabilities in the CRE sector to minimize potential
risks to financial stability. To ensure resilience in the
banking system, authorities should collect detailed
information on CRE exposures and conduct stress-test-
ing exercises that incorporate scenarios involving
large declines in CRE prices. The stress tests should
include smaller banks with material exposure to CREs.
Supervisors should also review banks’ assumptions
regarding CRE valuations and ensure that provisions
are adequate.

With private credit playing an increasingly sig-
nificant role in financial markets, it is imperative to
enhance reporting requirements to improve monitoring
and management of credit, liquidity, leverage, valua-
tions, and risks related to interconnectedness. Given
the potential macro criticality of private credit, coupled
with its exponential growth and increasing retail par-
ticipation, authorities may consider adopting a more
intrusive supervisory and regulatory approach.

CHAPTER 1

STEAdYING ThE COuRSE: FINANCIAL MARkETS NAvIGATE uNCERTAINTY

The buildup of debt amid elevated uncertainty
underscores the need to strengthen the macropru-
dential policy framework to contain excessive risk
taking in the nonbank financial sector and to ensure
that capital and liquidity buffers in banking sys-
tems are adequate to support the provision of credit
through periods of stress. Policymakers should tighten
appropriate macroprudential tools to increase resil-
ience against a range of shocks, as well as to forestall
further increases in pockets of elevated vulnerabilities,
while avoiding a destabilizing tightening of financial
conditions.

The tail of weak banks in the global financial sys-
tem and the risk of contagion to healthy institutions
highlight the urgent need to enhance financial sector
regulation and supervision. Despite repeated calls from
the Group of Twenty, some major jurisdictions that are
members of the Basel Committee have delayed imple-
menting the remaining elements of Basel III or have
introduced deviations from it, which could undermine
the effectiveness of the standard-setting process and
increase regulatory fragmentation. Full, timely, and
consistent implementation of Basel III and other inter-
national standards remains an important step.

Authorities should prepare to deal with financial
instability, including by ensuring that financial insti-
tutions are prepared to access central bank liquidity
and by intervening early to address liquidity stress in
the financial sector. All banks should be required to
test their access to central bank instruments period-
ically. Central banks should set up their frameworks
for emergency liquidity assistance in normal times,
anticipating that they will have to intervene in a crisis.
Central banks should be ready to provide liquidity
against a broad universe of assets while abiding by the
appropriate principles concerning solvency and viabil-
ity, collateralization, and appropriate haircuts. Further
progress on adopting and implementing frameworks
for recovery and resolution is critical to proactively
address the problems of weak or failing banks without
undermining financial stability or risking public funds.

Market turmoil in early August is a reminder of
how leveraged NBFIs can amplify stress. Even in the
absence of defaults, which could give rise to coun-
terparty risk and lead to contagion across financial
institutions, rapid unwinding of leveraged positions
can generate liquidity imbalances that amplify market
disruptions. One of the challenges in addressing these
issues is inadequate data, which hinder authorities’
ability to assess the vulnerabilities associated with non-
bank leverage and to identify large and concentrated
positions. It is crucial to enhance reporting require-
ments for nonbank institutions and to strengthen
policies that mitigate vulnerabilities and amplifica-
tion mechanisms stemming from nonbank leverage,
where judged to pose a threat to financial stability. In
addition, the growth of bond funds highlights the need
to reduce systemic risks by ensuring the effectiveness
of tools for managing liquidity. Timely and consis-
tent implementation of the Financial Stability Board’s
revised recommendations to address structural vulnera-
bilities from liquidity mismatches in open-ended funds
is crucial.

Data gaps often hinder the monitoring of intercon-
nectedness risks posed by pension funds and insurers.
Supervisors should fill data gaps and cooperate with
each other, including across borders, to ensure effective
monitoring of these risks. Cross-border cooperation
assumes importance in situations in which cross-bor-
der interconnections are significant and concentrated.
International bodies, such as the Financial Stability
Board, can aid in improving data gaps globally. In
jurisdictions in which defined-contribution pensions
and unit-linked insurance products are material,
supervisors should closely monitor the share of illiquid
investments held by these products. Liquidity stress
tests that consider scenarios involving crises related to
liquidity availability across the major asset classes are
important. It is also paramount to ensure the com-
patibility between the liquidity of assets and notice
periods required for clients to switch between different
investment products.

International Monetary Fund | October 2024

43

GLOBAL FINANCIAL STABILITY REPORT: STEAdYING ThE COuRSE: uNCERTAINTY, ARTIFICIAL INTELLIGENCE, ANd FINANCIAL STABILITY

Box 1.1. Synthetic Risk Transfers: Managing Risks or Creating New Ones?

An increasing number of banks around the world

have begun using synthetic risk transfers (SRTs) to
manage credit risk and lower capital requirements.
SRTs move the credit risks associated with a pool of
assets from banks to investors through a financial
guarantee or credit-linked notes while keeping the
loans on banks’ balance sheets. Through this credit
protection, banks can effectively claim capital relief
and reduce regulatory capital charges (Figure 1.1.1,
panel 1). However, the transactions can generate risks
to financial stability that need to be assessed and
monitored.

Globally, more than $1.1 trillion in assets have been

synthetically securitized since 2016, of which almost

two-thirds were in Europe (Figure 1.1.1, panel 2). In
the United States, activity picked up in 2023 and is
expected to accelerate further because the regulatory
landscape has become clearer. In Europe, corporate
and small- and medium-enterprise lending, a well-
known and stable loan category for investors, backs up
most of the issuance; recent transactions in the United
States have centered on retail loans, particularly auto-
mobile loans. In Europe, issuers of SRTs include global
systemically important banks and large banks, whereas
in the United States, regional banks issue SRTs as
well. Private credit funds are the dominant buyers,
with a market share exceeding 60 percent, followed by
pension funds, with close to 20 percent (Gonzalez and

Figure 1.1.1. Selected Information for SRT Structures, Issuance, and Returns

Banks use SRTs to oﬄoad risk and claim capital
relief.

Synthetic securitization volumes
have grown in recent years.

1. Stylized Example of a Direct Bank-Issued

2. Underlying Pool Size of

Synthetic Securitization at
Inception by Region
(Billions of US dollars)

EU
UK
Others

CLN Structure

Assumption: Tier 1 Capital Requirement of 10.5 percent

Without SRT

With SRT

Loan
portfolio
$100
(100%
RWA)

Reference
pool
$100

Tier capital
requirement:
$10.5
Bank

$87.50
(20%
RWA)

Senior
tranche
(retained)

$11.00
(0%
RWA)

$1.50
(1,250%
RWA)

Mezzanine
tranche

First loss
(retained)

Tier 1 capital
requirement:
$3.80
Bank

Principal and
interest payments
CLN

Senior class
Sub. class
Sub. class
Sub. class

Proceeds
of note sale and
credit protection

Investor

250

200

150

100

50

0

Credit risk indices suggest asset
quality in SRT reference pools is
starting to deteriorate,
particularly for SMEs.

3. Synthetic Risk Transfers:
SRTx™ Credit Risk Indices
(Indices)

SRTx™ CORP risk EU
SRTx™ CORP risk US
SRTx™ SME risk EU
SRTx™ SME risk US

100

80

60

40

7
1

8
1

9
1

0
2

1
2

2
2

3
2

6
1
0
2

3
2

.

p
e
S

3
2

.
v
o
N

4
2

.

n
a
J

4
2

.
r
a
M

4
2
y
a
M

4
2

.

g
u
A

3
2
0
2

.
l

u
J

Sources: Bloomberg Finance L.P.; International Association of Credit Portfolio Managers; Structured Credit Investor; and IMF staﬀ
calculations.

Note: Panel 1 shows a bank-issued CLN in which the bank buys protection for the mezzanine tranche by issuing CLNs to investors for cash up
front and retains the first-loss and senior tranches. Under securitization treatment, the senior tranche carries 20 percent in RWA, and the
first-loss tranche carries 1,250 percent in RWA. The RWA for the mezzanine tranche becomes zero because the bank is no longer exposed to
the losses from this tranche. In panel 3, each SRTx™ Credit Risk Index is compiled from contributor survey responses and measures market
sentiment regarding the direction of the SRT reference pool credit risk over the near term. The index scale ranges from 0 to 100, with levels
above 50 indicating a higher proportion of respondents estimating that credit risk is worsening. CLN = credit-linked note; RWA = risk-
weighted assets; SMEs = small and medium enterprises; SRT = synthetic risk transfer; SRTx™ CORP EU = credit risk index for large firms in
the European Union; SRTx™ CORP US = credit risk index for large firms in the United States; SRTx™ SME EU = credit risk index for small and
medium enterprises in the European Union; SRTx™ SME US = credit risk index for small and medium enterprises in the United States;
sub. = subordinate.

This box was prepared by Gonzalo Fernandez Dionis, Yiran Li, and Silvia L. Ramirez.

44

International Monetary Fund | October 2024

CHAPTER 1

STEAdYING ThE COuRSE: FINANCIAL MARkETS NAvIGATE uNCERTAINTY

Box 1.1 (continued)
Triandafil 2023). Industry estimates expect issuance
of SRTs to remain above $200 billion in Europe and
to more than triple in the United States to surpass
$50 billion in 2024 (Alloway 2024).

The amount of capital relief varies by transaction.
In Europe and the United States, the lack of data on
private transactions makes an aggregate calculation
of capital relief for banks challenging. Proceeds from
capital relief can be used to originate more loans, fund
stock repurchases, or pay dividends. If interest rates
fall, certain motivations behind SRTs become less rele-
vant. In addition, SRTs allow banks to limit loan book
concentration, reduce counterparty risk, and, for some
US banks, avoid realizing potential mark-to-market
losses linked to gyrations in interest rates compared
with an outright sale of the loans. Investors purchase
SRTs to access loan categories that may not be easily
accessible through public markets or direct lending
to earn attractive returns (8–12 percent) compared
with those from other asset classes as well as to meet
mandates to allocate capital in private credit.

However, certain SRT characteristics could increase

risks to financial stability. First, SRTs may elevate
interconnectedness and create negative feedback loops
during stress. For instance, there is anecdotal evidence
that banks are providing leverage for credit funds to
buy credit-linked notes issued by other banks. From a
financial system perspective, such structures retain sub-

stantial risk within the banking system but with lower
capital coverage. The magnitude of the interconnec-
tions is difficult to assess because the market remains
opaque, with only a fraction of deals being made
public and no centralized repository for data on SRTs.
Second, SRTs may mask banks’ degree of resilience
because they may increase a bank’s regulatory capital
ratio while its overall capital level remains unchanged.
Increased use of SRTs may reflect inability to build
capital organically because of weaker fundamentals and
profitability performance. Furthermore, overreliance
on SRTs exposes banks to business challenges should
liquidity from the SRT market dry up. Currently,
the asset pools being securitized seem to be of higher
quality; however, there are signs of increased concerns
regarding deterioration of asset quality (Figure 1.1.1,
panel 3). Financial innovation may lead to securiti-
zation of riskier asset pools, challenging banks with
less sophisticated tools for risk management, because
some more complex products make the identity of the
ultimate risk holder less clear. Finally, although lower
capital charges at a bank level are reasonable, given
the risk transfer, cross-sector regulatory arbitrage may
reduce capital buffers in the broad financial system
while overall risks remain largely unchanged. Financial
sector supervisors need to closely monitor these risks
and ensure the necessary transparency regarding the
SRTs and their impact on banks’ regulatory capital.

International Monetary Fund | October 2024

45

GLOBAL FINANCIAL STABILITY REPORT: STEAdYING ThE COuRSE: uNCERTAINTY, ARTIFICIAL INTELLIGENCE, ANd FINANCIAL STABILITY

Box 1.2. Interconnectedness Through Tokenization

Tokenization of real-world assets involves creating a
digital representation of these assets on a blockchain.1
Although it is not a new phenomenon, its adoption in
certain financial markets by large players could lead to
increased interconnectedness between the traditional
financial markets and crypto markets. In recent years,
tokenization of money market funds’ shares, repos,
and Treasuries has gained popularity because high
interest rates have allowed these products to offer high
yields, particularly in comparison to stablecoins (see
Figure 1.2.1). In addition, there are expectations that
tokenization may generate benefits such as potential
immediate trade settlement, lower costs related to
ownership, fractional use of safe and liquid collateral
for management of liquidity, and timely receipt of
asset yields or coupons.2 In regard to repos specifically,
investors reportedly seek the immediacy and cost-ef-
ficiency of blockchain-based transactions to manage
intraday liquidity, which helps mitigate mishaps that
might otherwise result in costly intraday repos with
central banks.3

Traditional finance institutions have shown
interest in participating in the tokenization wave.
BlackRock and Franklin Templeton have launched
tokenized Treasury funds, the BlackRock USD
Institutional Digital Liquidity Fund and Franklin
OnChain US Government Money Fund, respec-
tively. Major banks such as JPMorgan, UBS, and

This box was prepared by Gonzalo Fernandez Dionis and

Kleopatra Nikolaou.

1Digital tokens are assets issued on electronic ledgers that

are shared, trusted, and programmable (Agur and others,
forthcoming).

2Fractional ownership involves digitizing real-world assets and

representing them on a blockchain as tokens. Each token rep-
resents a fraction of the asset’s total value. Investors can purchase
these tokens and use them as collateral for loans.

Tokenized money market funds are digital representations of
shares in traditional money market funds on a blockchain. Toke-
nized repo is a type of short-term borrowing in fixed income
through the exchange of cash for tokenized collateral. Tokenized
Treasuries are digital representations of US Treasury securities in
the form of tradable tokens on the blockchain. In practice, how-
ever, tokenized Treasuries often refer to tokenized fund shares,
with Treasuries as the underlying collateral. Tokenized Treasuries
allow investors engaging in blockchain trading to receive interest
payments and principal repayments according to the Treasuries’
schedules and rates.

3For a quantitative assessment of the benefits from financial

asset tokenization, please refer to Box 2 in Agur and others
(forthcoming).

Figure 1.2.1. Interconnectedness Through
Tokenization

Tokenization of money market funds has gained momentum,
though it remains at a nascent stage, as key traditional finance
players have entered the market.

Tokenized Treasury Funds by Issuer
(Millions of US dollars)

Other Treasury funds
FOBXX
BUIDL

2,500

2,000

1,500

1,000

500

0
Jan.
2023

Apr.
23

Jul.
23

Oct.
23

Jan.
24

Apr.
24

Jul.
24

Sources: Bloomberg Finance L.P.; RWA.xyz; and IMF staff estimates.

Note: BUIDL = BlackRock USD Institutional Digital Liquidity Fund;
FOBXX = Franklin OnChain US Government Money Fund.

DBS are using systems such as Onyx and Broad-
ridge’s Distributed Ledger Repo to tokenize shares
of money market funds for use as collateral and to
execute intraday repos. Performance of tokenized
money market funds has been at par with that of
traditional money market funds; for example, the
Franklin OnChain US Government Money Fund’s
average annual return through the end of July 2024
was 5.3 percent, compared with 5.0 for Federated
Hermes’ Treasury Obligations Fund.

Concerns regarding financial stability stemming
from the tokenization of real-world assets are limited
at present, given its still-small scale. Over the medium
term, tokenization deepens the nexus between the
ecosystem of crypto assets and the traditional finan-
cial system. If more real-world assets become toke-
nized, the resulting increased interconnectedness can
transmit shocks or volatility from crypto markets to
the real-world markets of the underlying assets, or vice
versa. Volatility might arise, for example, if investors
become uncertain about the value of a token or the
possibility of redeeming it, or if a shock occurs during
a weekend, when the underlying real-world assets
cannot be traded or funded as opposed to tokenized

46

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CHAPTER 1

STEAdYING ThE COuRSE: FINANCIAL MARkETS NAvIGATE uNCERTAINTY

Box 1.2 (continued)
funds, which allow 24/7 trading (see Carapella and
others 2023). Concerns may also arise as a result of
technology risks and increased use of leverage through
tokenization. Finally, a rise in tokenized safe and
liquid assets, such as tokenized Treasuries, can interact
with the rise of stablecoins, especially considering that
many stablecoins do not offer returns. The growth

of this new form of financial intermediation will
depend also on regulatory developments, which can be
complex and evolve slowly. Supervisors in the financial
sector should continue to monitor risks related to
interconnectedness within the crypto markets and
between those markets and traditional capital markets
for potential increases in vulnerabilities.

International Monetary Fund | October 2024

47

GLOBAL FINANCIAL STABILITY REPORT: STEAdYING ThE COuRSE: uNCERTAINTY, ARTIFICIAL INTELLIGENCE, ANd FINANCIAL STABILITY

Box 1.3. Summertime Blues: The Carry Trade Unwind and VIX Surge of August 2024
Worse-than-expected labor market data in the

Carry trades involve borrowing money in currencies

with low funding costs and investing in assets in cur-
rencies with higher returns, allowing investors to earn
the spread, or “carry,” of relative returns.1 Carry trades
have been popular for many decades, and recently, the
divergence of monetary policies worldwide provided
investors with opportunities to increase exposures to
them. Various funding currencies have been used for
these trades over time. Over the past several years,
the relatively low interest rates in Japan vis-à-vis in
other advanced economies have driven the yen as the
preferred funding currency over others. Although it
is difficult to estimate the overall size of carry trade
positions (see BIS 2024), the amount of Japanese yen
borrowed by nonresidents, who do not naturally need
Japanese yen, could serve as one guide to an upper
bound of the estimate (Figure 1.3.1, panel 1). Many
investors have reportedly used the Japanese yen as a
funding currency to invest in Brazilian and Mexican
government bonds, Indian equities and corporate
bonds, and US technology stocks in artificial intelli-
gence. Absent any significant changes in the exchange
rate or relative interest rates, this type of trade can
generate large profits over time. However, carry trades
are inherently unstable: When currency and interest
rate volatilities surge, the carry may no longer be there,
and profits from the carry trade can be quickly wiped
out.2 Carry trades therefore tend to accumulate grad-
ually during periods of sustained low volatility, when
leverage and risk taking accumulate—for example,
borrowing yen to invest in Mexican pesos—but can
unwind rapidly and in large volumes when conditions
turn adverse, potentially destabilizing markets.

This box was prepared by Deepali Gautam, Sanjay Hazarika,
Harrison Kraus, Mustafa Oguz Caylan, and Aki Yokoyama. The
box provides an update on market developments since the April
2024 Global Financial Stability Report.

1Carry trade refers to borrowing at a low interest rate and
investing in a high-return asset but is best known for those con-
ducted between currencies. Many are executed via off-balance-
sheet derivatives that are partially reflected in on-balance-sheet
statistics. For example, foreign exchange swaps typically exchange
the notional amount in two different currencies for each party
requiring financing. Meanwhile, currency options may not be
fully reflected.

2While the difference in interest rates between two currencies,

or “carry” earned over the course of a year, is only single-digit
percentage, exchange rates can move more than 10 percent once
they start to move, and the capital loss from a market move in
an unwanted direction can easily exceed the carry earned over
time.

United States following the Bank of Japan’s monetary
policy decision in July meant that carry trades were
no longer profitable, and their unwinding led to
spikes in stock and currency volatility in early August.
The interest rate differential between the dollar and
yen narrowed, and the yen appreciated in a speed-up
of trends that began in July (Figure 1.3.1, panel 2).
High-yielding currencies that were targets of carry
trades depreciated (Figure 1.3.1, panel 3). At the
same time, the Nikkei index experienced a collapse of
12.4 percent on August 5, its largest one-day move
since 1987. In the United States, the Chicago Board
Options Exchange Volatility Index (VIX) surged from
16 to more than 65, before lowering to 37 by the
end of the day. Other major indices such as the S&P
500 (–3 percent) and the STOXX Europe 600 (–2
percent) also lost ground, as few stocks were spared.
Anecdotally, along with the unwind of carry trades
by nonbank financial intermediaries like hedge funds,
momentum and algorithmic traders also fed into the
sell-off, guided by their trend-following algorithms, as
did broker–dealers who were selling stocks to hedge
risks created by selling large amounts of options to
their clients.

This period of high volatility was ultimately

short-lived and risk assets regained most of the losses
in subsequent days, indicating that traders did not
see the declines to be justified by macroeconomic
fundamentals. The massive move in the VIX did not
trigger a more widespread US sell-off, as the signal of
excessive volatility it was conveying largely reflected
issues related to the index’s construction, rather than
actual market transactions (possibly because out-of-
the-money options currently play a much larger role
than they did in previous years).3 Front-end VIX
futures saw much smaller moves on August 5 than the
index itself (Figure 1.3.1, panel 4). In contrast, during
past episodes such as the COVID-19 pandemic

3The VIX is effectively a weighted average of the midpoints of

the bid and ask prices of multiple option contracts; the weight
used is proportional to 1 divided by the strike price squared
(CBOE 2022). By construction, the weight of the far-out-of-the-
money put option with a lower strike price is more significant,
and illiquid market conditions easily lead to higher midpoint
prices as sellers pull back their ask prices. Front VIX futures
expire on August 21, and, therefore, the much lower levels of
futures prices relative to the VIX indicate that traders did not
view the high VIX level to be sustained for more than two
weeks.

48

International Monetary Fund | October 2024

CHAPTER 1

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Box 1.3 (continued)

and the 2015 China devaluation, the VIX and VIX
futures surged in tandem. The episode highlights the
potentially destabilizing role that leveraged strate-
gies such as carry trades can play in global markets,
underscoring the need for more regulatory scrutiny,
especially in regard to nonbank financial intermediar-
ies. It is also a reminder that the disconnect between

heightened uncertainty and low market volatility may
abruptly close, with adverse consequences for asset
prices. Carry trades have thrived in various forms as
a result of the prolonged low-yield and low-volatility
environment in the past, and it remains to be seen
how large the unwinding of these positions could be
in the future.

international Monetary Fund | october 2024

49

Lending by banks to all borrowers outsideof JapanLending by banks to non-bank borrowers outside of Japan (right scale)US jobs report for the month of JulyBOJ meetingTOPIX (right scale)NikkeiUSD-JPY (right scale)US jobs report for the month of JulyBOJ meetingUSD-JPY real OIS diﬀerentialFront VIX futures contractVIX indexCurrency returns from start of 2024 until USDJPY is near 162Currency returns after USDJPY is near 162Year-to-date interest rate return against USD (right scale)Figure 1.3.1. Unwinding of Carry Trades in Early AugustCross-border borrowing in yen has risen sharply in recent years.1. International Bank Claims Denominated in Japanese Yen(Trillions of Japanese yen)5010015020010153020403525455020222423The Bank of Japan’s monetary policy decision and a weaker-than-expected US jobs report was followed by the unwinding of carry trades.2. US-Dollar-to-Japanese-Yen Spot Rate versusRate Diﬀerential and Major Japanese Equity Indices(Percentage points, top left scale; yen per dollar,top right scale; index, bottom scales)1,8002,2002,6003,00024,00029,00034,00039,00044,00020222423100120140160−1.75−0.750.251.252.2520070809101112131415161718192021222324Unwinding of carry trades led to depreciation of high- yielding currencies and appreciation of low-yielding ones.3. Currency Returns(Percentage points)−20−10−15−5051015−2024681001Aug10:1402 Aug4:3002 Aug15:0105Aug9:3106 Aug3:3306Aug13:3507Aug7:5108Aug0:4108Aug12:0909Aug6:2509Aug16:56Traders did not believe the spike in the VIX was sustainable.4. Intraday VIX versus Front VIX Futures Contract(Percent, intraday moves on August 5, 2024)010203040506070COPMXNBRLZARHUFIDRINRCLPPLNPENPHPKRWTHBMYRJPYSources: Bank for International Settlements; Bloomberg Finance L.P.; and Haver Analytics.Note: In panel 4, the front VIX futures contract is a generic first future of the VIX, which has a one-month maturity. BOJ = Bank of Japan; BRL = Brazilian real; CLP = Chilean peso; COP = Colombian peso; CPI = consumer price index; HUF = Hungarian forint; IDR = Indonesian rupiah; INR = Indian rupee; JPY = Japanese yen; KRW = Korean won; MXN = Mexican peso; MYR = Malaysian ringgit; OIS = overnight index swap; PEN = Peruvian sol; PHP = Philippine peso; PLN = Polish zloty; THB = Thai baht; TOPIX = Tokyo Stock Price Index; USD = US dollar; VIX = Chicago Board Options Exchange Volatility Index; ZAR = South African rand.GLOBAL FINANCIAL STABILITY REPORT: STEAdYING ThE COuRSE: uNCERTAINTY, ARTIFICIAL INTELLIGENCE, ANd FINANCIAL STABILITY

Box 1.4. Domestic Investors in Local Bond Markets: A Stabilizing Force?

Local bond markets in emerging markets have been

growing over the years. Alongside banks, nonbank
financial intermediaries—especially pension and insur-
ance funds—are playing an increasingly important
role in markets for local currency government bonds
(LCGBs). Total assets under management in nonbank
financial intermediaries have grown since 2002 to
reach 25 percent of GDP in the median emerging
market from less than 10 percent as recently as 2003.
The importance of these funds has grown since the
end of the pandemic, as the role of foreign investors
has declined and domestic banks have shed some of
their holdings of domestic government bonds acquired
during the early stages of the pandemic. The recent
increase of the share of holdings by nonbank finan-
cial intermediaries has varied across countries, with
pension funds playing an increasing role, as various
countries have recently either instituted or proposed
new rules that could significantly increase the amount
of assets these funds hold.

Long-term domestic institutional investor funds
tend to focus their investments in LCGBs, providing
governments with a stable source of funding as other
asset classes such as domestic corporate bonds and
equities are comparatively less developed. The rise
of this investor base has reduced reliance on foreign
capital, mitigating risks of capital outflows; enhanced
market depth and liquidity; and reduced volatility.
Countries in which the share of domestic government
bonds held by pension and insurance companies has
increased have experienced less volatility in term pre-
miums, whereas those in which the share has declined
have seen term premiums rise (Figure 1.4.1, panel 1).
In addition, a sizable domestic investor base with a
long-term investment horizon could mitigate a rise of
a sovereign-bank nexus, such as the one that occurred
during the pandemic (see Chapter 2 of the April 2022
Global Financial Stability Report).

The greater presence of long-term domestic institu-
tional investors could have important implications for

Figure 1.4.1. NBFIs Play an Important Role in NBFI Markets

Countries with a rising share of holdings among pension and
insurance funds broadly experienced less pressure on term
premiums.

The availability of long-dated and inflation-linked assets
varies significantly across jurisdictions.

1. Change in Term Premiums for Local Currency 10-Year

2. Local Government Bonds That Have Remaining

Government Bonds, March 2022 to June 2024
(Cumulative change to z-scores on domestic term premiums
since the onset of the Federal Reserve’s hiking cycle)

Maturities of at Least 10 Years or Are Linked to Inflation
(Percent)

4

2

0

−2

Maturity greater than 10 years
Inflation linked

EMs with declining presence (n = 4) average
EMs with increasing presence (n = 7) average

Sep.
22

Mar.
23

Sep.
23

Mar.
24

−4

Mar.
2022

F
A
Z

D
N

I

L
O
C

S
Y
M

X
E
M

N
D

I

A
R
B

N
U
H

L
H
C

R
E
P

L
H
P

R
U
T

L
O
P

80

70

60

50

40

30

20

10

0

Sources: Arslanalp and Tsuda 2014; Bank for International Settlements; Bloomberg Finance L.P.; Financial Stability Board; and IMF staﬀ
calculations.

Note: In panel 1, the sample includes 11 major emerging markets. Local currency government bond holdings by pension and insurance
funds are assessed from December 2021 to December 2023. Term premiums are on the 10-year yield and follow the methodology in Adrian,
Crump, and Moench (2013). Data labels in the figure use International Organization for Standardization (ISO) country codes. EMs =
emerging markets.

This box was prepared by Jeffrey Williams.

50

International Monetary Fund | October 2024

CHAPTER 1

STEAdYING ThE COuRSE: FINANCIAL MARkETS NAvIGATE uNCERTAINTY

Box 1.4 (continued)
decisions by government agencies charged with debt
management. These funds tend to prefer longer-dated
securities, as well as inflation-linked assets, to better
match their liability structures. Such instruments
are already a large fraction of LCGBs outstanding in
several countries (Figure 1.4.1, panel 2), and in others,
this growing asset class could present an opportunity
to extend the domestic yield curve. Although the
growth of this investor base could present a stabilizing

force for LCGB markets, it also presents risks. With
limited alternative options for domestic investment,
funds may become overly concentrated in LCGB,
leaving them vulnerable to large losses should interest
rates rise precipitously, the yield curve steepen sharply,
or inflation surge. Additionally, from the government’s
point of view, an unexpected increase in redemptions
from these funds could drive a sudden rise in the cost
of domestic funding.

International Monetary Fund | October 2024

51

GLOBAL FINANCIAL STABILITY REPORT: STEAdYING ThE COuRSE: uNCERTAINTY, ARTIFICIAL INTELLIGENCE, ANd FINANCIAL STABILITY

References

Abboud, Alice, Chris Anderson, Aaron Game, Diana Iercosan,
Hulusi Inanoglu, and David Lynch. 2021. “Banks’ Backtest-
ing Exceptions during the COVID-19 Crash: Causes and
Consequences.” FEDS Notes, Federal Reserve Board, July 18.
https://www.federalreserve.gov/econres/notes/feds-notes/
banks-backtesting-exceptions-during-the-covid-19-crash-caus-
es-and-consequences-20210708.html.

Abrahams, Michael, Tobias Adrian, Richard K. Crump, Eman-
uel Moench, and Rui Yu. 2016. “Decomposing Real and
Nominal Yield Curves.” Journal of Monetary Economics 84:
182–200.

Adrian, Tobias, Nina Boyarchenko, and Or Shachar. 2017.

“Dealer Balance Sheets and Bond Liquidity Provision.” Staff
Report 803, Federal Reserve Bank of New York, New York.

Adrian, Tobias, Richard K. Crump, and Emanuel Moench.

2013. “Pricing the Term Structure with Linear Regressions.”
Journal of Financial Economics 110 (1): 110–38.

Adrian, Tobias, Fabio Natalucci, and Jason Wu. 2024. “Emerging
Markets Navigate Global Interest Rate Volatility.” IMF Blog,
January 31. https://www.imf.org/en/Blogs/Articles/2024/01/31/
emerging-markets-navigate-global-interest-rate-volatility.

Agur, Itai, Germán Villegas-Bauer, Tommaso Mancini-Griffoli,
Maria Soledad Martinez Peria, and Brandon Tan. Forth-
coming. “Trading Tokens: Benefits and Risks of Tokenized
Financial Markets.”

Alloway, Tracy. 2024. “One of the Hottest Trades on Wall Street,
an Etymological Study.” Markets: Odd Lots (blog), Bloomberg,
June 27. https://www.bloomberg.com/news/articles/2024-06-27/
one-of-the-hottest-trades-on-wall-street-an-etymological-study.

Aquilina, Matteo, Macro Lombardi, Andrea Schrimpf, and

Vladyslav Sushko. 2024. “The Market Turbulence and Carry
Trade Unwind of August 2024.” BIS Bulletin 90, Bank for
International Settlements, Basel.

Arslanalp, Serkan, and Takahiro Tsuda. 2014. “Tracking Global

Demand for Emerging Market Sovereign Debt.” IMF
Working Paper 14/039, International Monetary Fund, Wash-
ington, DC.

Atkeson, Andrew G, Andrea L. Eisfeldt, and Pierre-Olivier Weill.

2013. “Measuring the Financial Soundness of US Firms,
1926-2012.” NBER Working Paper 19204, National Bureau
of Economic Research, Cambridge, MA.

Baker, Scott R., Nicholas Bloom, and Steven J. Davis. 2016.

“Measuring Economic Policy Uncertainty.” Quarterly Journal
of Economics 131 (4): 1593–636.

Berlin, Andrew. 2024. “Why Is There a Disparity in Private

Credit Default Rates?” News, Loan Syndications and Trading
Association, May 15.

Bradley, Annika. 2023. “Is Your Industry Super Fund
Too Illiquid?” Morningstar, May 10. https://www.
morningstar.com.au/insights/retirement/234919/
is-your-industry-super-fund-too-illiquid.

52

International Monetary Fund | October 2024

Breeden, Sarah. 2022. “Risks from Leverage: How Did a Small
Corner of the Pensions Industry Threaten Financial Stabil-
ity?” Speech delivered at the International Swaps and Deriva-
tives Association and the Alternative Investment Management
Association, Brussels, Belgium, November 7.

Brunnermeier, Markus K., and Lasse H. Pedersen. 2009. “Mar-
ket Liquidity and Funding Liquidity.” Review of Financial
Studies 22 (6): 2201–38.

Cai, Yuhua, Anna Helmke, Benjamin Mosk, and Felix Sun-

theim. 2024. “Investment Funds and Corporate Bond Market
Stability.” Unpublished, International Monetary Fund,
Washington, DC.

Caldara, Dario, and Matteo Iacoviello. 2022. “Measuring Geopo-
litical Risk.” American Economic Review 112 (4): 1194–225.
Carapella, Francesca, Grace Chuan, Jacob Gerszten, Chelsea

Hunter, and Nathan Swem. 2023. “Tokenization: Overview
and Financial Stability Implications.” Finance and Eco-
nomics Discussion Series 2023-060, Board of Governors of
the Federal Reserve System, Washington, DC. https://doi.
org/10.17016/FEDS.2023.060.

Cerutti, Eugenio M., Jiaqian Chen, and Martina Hengge. 2024.
“A Primer on Bitcoin Cross-Border Flows: Measurement.”
IMF Working Paper 24/085, International Monetary Fund,
Washington, DC.

Chicago Board Options Exchange (CBOE). 2022. “Volatility
Index Methodology: Cboe Volatility Index.” Cboe Global
Indices, LLC, Chicago, IL.

Climate Policy Initiative (CPI). 2023. “Global Landscape of

Climate Finance 2023.” San Francisco.

Deghi, Andrea, Junghwan Mok, and Tomohiro Tsuruga. 2021.

“Commercial Real Estate and Macrofinancial Stability During
COVID-19.” IMF Working Paper 21/264, International
Monetary Fund, Washington, DC.

Diebold, Francis X., and Kamil Yilmaz. 2009. “Measuring

Financial Asset Return and Volatility Spillovers, With Appli-
cation to Global Equity Markets.” The Economic Journal 119:
158–71.

Ellias, Jared A., and Elisabeth de Fontenay. 2024. “The Credit
Markets Go Dark.” Public Law and Legal Theory Paper
2024-45, Duke Law School, Durham, NC. https://ssrn.com/
abstract=4879742.

Financial Stability Board (FSB). 2017. “Implementation and
Effects of the G20 Financial Regulatory Reforms: Third
Annual Report.” Basel, Switzerland.

Financial Stability Board (FSB). 2023. “Global Monitoring Report
on Non-Bank Financial Intermediation.” Basel, Switzerland.
Goldman Sachs. 2023. “Balanced Bear Returns: Bear Steepening
Drives Another 60/40 Drawdown (Mueller-Glissmann).”
Global Markets Daily, Goldman Sachs International, London.
Gonzalez, Fernando, and Cristina Morar Triandafil. 2023. “The
European Significant Risk Transfer Securitization Market.”
Occasional Paper 23, European Systemic Risk Board, Frank-
furt am Main, Germany.

CHAPTER 1

STEAdYING ThE COuRSE: FINANCIAL MARkETS NAvIGATE uNCERTAINTY

Hunt, Jeremy. 2023. “Chancellor Jeremy Hunt’s Mansion House

Speech.” Speech delivered at Mansion House, London,
July 10.

International Monetary Fund (IMF). 2024a. “Germany: Selected

Issues.” IMF Country Report 24/230, Washington, DC.
International Monetary Fund (IMF). Forthcoming. “Unlocking
Adaptation Finance in Emerging Markets and Developing
Economies.” IMF Staff Climate Note, International Monetary
Fund, Washington, DC.

JPMorgan. 2024. “Fresh Momentum for the Reformed Euro-
pean Long-Term Investment Fund (ELTIF).” New York.
Kogan, Joseph, Romina Kazandjian, Shijia Luo, Moustapha
Mbohou Mama, and Hui Miao. 2024. “The Role of IMF
Arrangements in Restoring Access to International Capital
Markets.” IMF Working Paper 24/173, International Mone-
tary Fund, Washington, DC.

Kuttner, Kenneth H. 2006. “Can Central Banks Target Bond
Prices?” In Monetary Policy in an Environment of Low Infla-
tion, edited by Kyuil Chung. Seoul, Korea: Bank of Korea.
Lucas, Robert E. 1978. “Asset Prices in an Exchange Economy.”

Econometrica 46 (6): 1429–45.

McDonnell, Claire. 2024. “Private Credit 101: High Rates,

articles/private-credit-101-high-rates-scarce-exits-prompt-in-
clusion-of-equity-warrants.

Organisation for Economic Co-operation and Development

(OECD). 2023. “Climate Finance Provided and Mobilised by
Developed Countries: Aggregate Trends.” OECD Publishing,
Paris, France.

Pazarbasioglu, Ceyla. 2024. “Sovereign Debt Restructuring Pro-
cess Is Improving amid Cooperation and Reform.” IMF Blog,
June 26. https://www.imf.org/en/Blogs/Articles/2024/06/26/
sovereign-debt-restructuring-process-is-improving-amid
-cooperation-and-reform.

Pensions&Investments. 2023. “Private Markets Get on the DC
Plan Investment Menu.” Investment Insights, December 11.
Pensions&Investments, New York.

Sifma. 2024. “US Mortgage-Backed Securities Statistics.”
https://www.sifma.org/resources/research/us-mortgage
-backed-securities-statistics/.

Standard Chartered Bank. 2024. “Guide for Adaptation and

Resilience Finance.” Standard Chartered Bank, London, UK.
UN Environment Programme (UNEP). 2023. “Adaptation Gap
Report 2023.” UNEP, Nairobi, Kenya. https://www.unep.org/
resources/adaptation-gap-report-2023.

Scarce Exits Prompt Inclusion of Equity Warrants.” Private
Credit, Pitchbook, July 25. https://pitchbook.com/news/

Verdelhan, Adrien. 2018. “The Share of Systematic Variation in
Bilateral Exchange Rates.” Journal of Finance 73(1): 375–418.

International Monetary Fund | October 2024

53

MACROFINANCIAL STABILITY AMID HIGH GLOBAL
ECONOMIC UNCERTAINTY

Chapter 2 at a Glance
 • Uncertainty regarding global economic outcomes and policies has been higher since the COVID-19 pan-

demic amid inflation shocks and rising geopolitical tensions.

 • High macroeconomic uncertainty can profoundly affect macrofinancial stability by exacerbating downside

market tail risks, delaying consumption and investment decisions, and reducing credit supply.

 • As financial indicators may not fully capture macroeconomic uncertainty, particularly in economies with
less-developed financial systems, there is merit in considering measures of macroeconomic uncertainty in
systemic risk assessment frameworks such as the growth-at-risk framework.

 • An increase in macroeconomic uncertainty equivalent to its rise during the global financial crisis

reduces the downside outcome (the 10th percentile) of one-year-ahead real GDP growth by, on average,
1.2 percentage points in advanced and emerging market economies.

 • Macroeconomic uncertainty also tends to amplify the effect of prevailing macrofinancial vulnerabilities,
such as excessive leverage in the private and public sectors, on downside risks to future output growth.
 • A significant easing of financial conditions amid high macroeconomic uncertainty can exacerbate downside
risks to future output growth, particularly during periods of low financial market volatility (that is, during
“macro-market disconnects”). An adequate macroprudential policy response can, however, mitigate this effect.
 • The effects of macroeconomic uncertainty can spill over across borders through trade and financial inter-

linkages, increasing the risk of contagion in the face of adverse shocks.

Policy Recommendations
 • Reduce domestic macroeconomic uncertainty by strengthening the credibility and transparency of frame-
works for monetary, fiscal, and financial sector policies and through effective communication strategies.

 • Implement adequate fiscal and macroprudential policies to contain macrofinancial vulnerabilities and

build resilience against adverse shocks, particularly when macroeconomic uncertainty is high.

 • Build adequate international reserve buffers and allow exchange rate flexibility to help cushion the adverse

spillover effects of an increase in foreign macroeconomic uncertainty.

 • Devote resources to quantifying, managing, and mitigating the risks from rising geopolitical uncertainty

on macrofinancial stability.

Introduction

A high level of uncertainty has characterized the
global macroeconomic landscape since the COVID-19
pandemic. Uncertainty about economic outcomes and
policies spiked during the COVID-19 pandemic and
has remained high since then, compared with levels in

This chapter was prepared by Rafael Barbosa, Yuhua Cai, Mario
Catalán (co-lead), Andrea Deghi (co-lead), Li Lin, Tatsushi Okuda,
Mustafa Yenice, and Aleksandr Zotov, under the guidance of
Mahvash Qureshi. Ian Dew-Becker and Stefano Giglio served as
external advisors.

earlier years, amid inflation shocks, escalating geopolit-
ical tensions, rapidly emerging new technologies, and
increasing climate-related risks.1 Different measures
of macroeconomic uncertainty make this evident
(Figure 2.1).2

1In economics, uncertainty refers to situations in which future

outcomes are difficult to predict (Knight 1921).

2Measures of macroeconomic uncertainty tend to be volatile but
on average have stayed elevated since the pandemic. Some measures,
such as global economy policy uncertainty of Baker, Bloom, and
Davis (2016), declined in the first quarter of 2024 but rose again in
the second quarter amid electoral uncertainty in some major econo-
mies (Online Annex Figure 2.1.1).

International Monetary Fund | October 2024

55

CHAPTER2GLOBAL FINANCIAL STABILITY REPORT: STEAdYING ThE COuRSE: uNCERTAINTY, ARTIFICIAL INTELLIGENCE, ANd FINANCIAL STABILITY

Figure 2.1. Economic and Policy Uncertainty

Uncertainty around the macroeconomic outlook has increased ...

... along with policy uncertainty.

1. US and Non-US Real Economic Uncertainty Index, 2010–23

2. Global Economic Policy Uncertainty, 2010–24:Q2

(Index, standardized)

(Index)

3.0

2.5

2.0

1.5

1.0

0.5

0

−0.5

−1.0

2010–14

2015–19

2020–21

2022–23

2010–14

2015–19

2020–21

2022–24:Q2

US

Non-US

Global economic policy
uncertainty index

Monetary policy
uncertainty index (BBD)

Monetary policy
uncertainty index (HRS)

300

250

200

150

100

50

0

Sources: Baker, Bloom, and Davis 2016; Husted, Rogers, and Sun 2020; IMF, Global Data Source and International Financial Statistics databases; Organisation for
Economic Cooperation and Development, Main Economic Indicators database; and IMF staﬀ calculations.

Note: Panel 1 shows values on a real economic uncertainty index for the United States and other economies based on the approach in Ludvigson, Ma, and Ng (2021). The
index is standardized over its historical mean (January 1990 through September 2023) and averaged across diﬀerent periods. Non-US real economic uncertainty index is a
GDP-weighted average across non-US economies. In panel 2, “Global economic policy uncertainty index” is a GDP-weighted index from Baker, Bloom, and Davis (2016)
averaged over the indicated time period. For additional details on the data sources, see Online Annex 2.1. The monetary policy uncertainty indices pertain to the United
States and are compiled by Baker, Bloom, and Davis (BBD, 2016) and Husted, Rogers, and Sun (HRS, 2020), respectively.

Increased macroeconomic uncertainty can profoundly

Macroeconomic uncertainty can interact with

affect macrofinancial stability. High macroeconomic
uncertainty can potentially affect macrofinancial sta-
bility—or systemic risk—through three key channels.3
First, it can exacerbate downside market tail risks in the
event of an adverse shock (the market channel). Second,
it can delay private sector consumption and investment
decisions, slowing economic activity and raising credit
risks for financial institutions that can in turn trigger an
adverse macrofinancial feedback loop (the real channel).
And third, it can reduce the supply of domestic credit
by financial institutions by exacerbating challenges in
determining the creditworthiness of new borrowers (the
credit channel). These three channels can interact and
mutually reinforce each other, amplifying the effect of
macroeconomic uncertainty on macrofinancial stability.4

3Macrofinancial stability is defined in terms of systemic risk, that
is, the risk of disruption to the financial system that can have serious
negative consequences for the real economy, and is measured by
downside tail risks to future real GDP growth.

4While studies using well-known measures of macroeconomic uncer-

tainty generally find it to be negatively associated with asset returns
and volatility (Asgharian, Christiansen, and Hou 2015; Bali, Brown,
and Tang 2017), the effect could also be positive. For example, high-
tech revolutions promising future productivity gains can be a source
of positive or “good” uncertainty, while geopolitical conflicts can be
considered as a source of negative or “bad” uncertainty (Bloom 2014;
Segal, Shaliastovich, and Yaron 2015; Dew-Becker and Giglio 2023).

56

International Monetary Fund | October 2024

potential vulnerabilities in the real and financial sectors
to magnify the effects of adverse shocks. For example,
in the presence of high levels of public debt relative
to GDP, investors may react more strongly to an
expansionary fiscal shock when uncertainty regarding
the economic outlook is high instead of low, leading
to a sharp increase in sovereign bond yields (see the
October 2024 Fiscal Monitor). Periods of high mac-
roeconomic uncertainty may also make the corporate
debt market more vulnerable to adverse shocks, partic-
ularly when leverage in the corporate sector is high or
credit spreads are perceived by investors to be overly
compressed. Equity markets are also likely to expe-
rience larger price corrections in the face of adverse
shocks when uncertainty about the macroeconomic
outlook is high and valuations are stretched relative to
fundamentals.5 These considerations may be particu-
larly pertinent at the current juncture as, along with
macroeconomic uncertainty, macrofinancial vulnerabil-
ities remain elevated (Online Annex Figure 2.1.1).
The effect of macroeconomic uncertainty can
spill over across borders. Global financial and real

5The set of shocks can include shocks to uncertainty itself that
drive aggregate fluctuations (Bloom 2009; Bloom and others 2018).

CHAPTER 2  MACROFINANCIAL STABILITY AMId hIGh GLOBAL ECONOMIC uNCERTAINTY

interconnectedness implies that increased macroeco-
nomic uncertainty can have cross-border implications
through the aforementioned channels. For exam-
ple, an increase in macroeconomic uncertainty that
imposes losses on investors in a particular region may
force them to sell assets in other countries, leading to
large asset price declines and triggering international
financial contagion.6 Similarly, by reducing domes-
tic consumption and investment, macroeconomic
uncertainty can weaken the demand for imports,
raising downside risks to economic activity in trading
partner countries.

Financial variables may not fully span macroeco-

nomic uncertainty. Existing approaches to assess
macrofinancial stability typically consider selected
financial indicators, including those related to financial
market uncertainty (for example, the Chicago Board
Options Exchange Volatility Index ), as relevant
variables in frameworks to assess systemic risk (Adrian,
Boyarchenko, and Giannone 2019; Adrian and others
2019). However, financial indicators may not fully
reflect macroeconomic uncertainty, making it useful to
consider it in frameworks to assess systemic risk and
predict tail risks to markets and economic activity.7
This may be particularly relevant for countries with
less developed financial markets or during episodes
of “macro-market disconnect”—that is, when mac-
roeconomic uncertainty is high and financial market
volatility (realized and implied) is low.8

Against this background, this chapter examines

risks to macrofinancial stability posed by macro-
economic uncertainty. The chapter first lays out a
simple conceptual framework for discussing the main

6Bond and stock market volatility tend to be positively correlated
across major economies, and this correlation seems to have increased
since the pandemic (Online Annex 2.1), suggesting that stress in
asset markets can spread quickly across the financial system.

7See, for example, Valkanov and Zhang (2018) and Dew-Becker
and Giglio (2023). Online Annex 2.2 shows that financial variables
explain about 80 percent of the variation in commonly used mea-
sures of macroeconomic uncertainty for advanced economies like
the United States, and 40 to 50 percent of the variation in those
for major emerging markets such as Brazil. This is because available
financial instruments may not fully hedge important risks facing
households and firms—for example, those related to housing mar-
kets (Shiller 2003, 2013; Benford, Ostry, and Shiller 2018).

8Several factors can drive macro-market disconnects, including
investor perception that future policy reactions will protect against
downside market risks. Bialkowski, Dang, and Wei (2022) show that
low-quality political signals, higher divergence in opinions among
investors, and strong equity market performance drive disconnects
between the VIX and US economic policy uncertainty. Todorov and
Vilkov (2024) note the role played by hedging of covered calls in
keeping the VIX at a low level in recent years.

channels through which macroeconomic uncertainty
can undermine macrofinancial stability, measured by
downside risks to real GDP. It then uses panel data
from a sample of 43 advanced and emerging market
economies since 1990 (or the earliest year for which
data are available) to empirically address three key
questions.9 First, does macroeconomic uncertainty
help predict downside risks to output? Second,
how does macroeconomic uncertainty interact with
macrofinancial vulnerabilities to affect downside risks
to output? Third, does the effect of macroeconomic
uncertainty spill over across borders to affect down-
side risks to economic activity in a country’s major
financial and trading partners? The chapter then
discusses policy options to mitigate the risks posed by
high macroeconomic uncertainty.

To assess the downside risk to future economic
activity from macroeconomic uncertainty, the chapter
extends the growth-at-risk (GaR) framework. Since
the global financial crisis, significant progress has
been made in systemic risk analytics. The GaR frame-
work (Adrian, Boyarchenko, and Giannone 2019)
has become an operational cornerstone in this regard,
providing a quantitative tool to assess the effect of
financial conditions on downside tail risks to real GDP
growth.10 The chapter builds on this framework in two
dimensions. First, it augments the GaR model with
measures of macroeconomic uncertainty to examine
if these are associated with downside tail risks to real
GDP growth. In this context, the chapter considers
three types of commonly used macroeconomic uncer-
tainty measures—those based on (1) the accuracy and
dispersion of forecasts for key macroeconomic variables,
(2) domestic policies, and (3) geopolitical tensions.
Second, the chapter implements the augmented GaR
framework using machine learning tools, in addition to
the standard panel quantile regressions, to exploit their
advantages in prediction and improve the forecasting of
downside tail risks to future GDP growth.11

9The cross-country sample coverage varies across exercises depend-

ing on data availability. See Online Annex 2.1 for information on
countries included in the sample and the data sources.

10Downside risks to future GDP growth are typically captured by
the 5th or 10th percentile of the distribution, while financial condi-
tions are proxied by a composite indicator of risky asset prices (such
as equity and corporate bond returns, real house price growth, etc.)
and measures of financial uncertainty (such as the VIX).

11Machine learning models have gained popularity for forecasting

economic and financial variables as they can accommodate many
predictors and complex, nonlinear relations between variables
(Gu, Kelly, and Xiu 2020; Coulombe and others 2022; Lenza,
Moutachaker, and Paredes 2023).

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57

GLOBAL FINANCIAL STABILITY REPORT: STEAdYING ThE COuRSE: uNCERTAINTY, ARTIFICIAL INTELLIGENCE, ANd FINANCIAL STABILITY

Figure 2.2. Macroeconomic Uncertainty and Macrofinancial Stability: Channels of Transmission

Macrofinancial imbalances act as amplifiers

Risks to macrofinancial stability

Uncertainty

Adverse
shock

Market sentiment
shifts

Investment
consumption
(Real channel)

Bank and
nonbank lending
(Credit channel)

Financial
conditions
(Market channel)

Lower growth

High asset market
volatility and
market tail risks

Source: IMF staﬀ elaboration.

Macroeconomic Uncertainty and
Financial Stability: A Conceptual
Framework

Macroeconomic uncertainty can affect macrofi-
nancial stability through three key channels. First, it
can affect macrofinancial stability through a market
channel, whereby macroeconomic uncertainty can
amplify the impact of adverse shocks on investor
sentiment, raising downside market tail risk—that is,
the risk of large negative realized future asset returns
(Figure 2.2) (Baker and Wurgler 2006; Birru and
Young 2022). The realization of market tail risks
could be transmitted to the broader economy through
balance sheet and financial acceleration effects, raising
downside risks to output (Adrian and others 2019).
Second, macroeconomic uncertainty can affect mac-
rofinancial stability through a real channel, whereby in
response to higher macroeconomic uncertainty, firms
and consumers may adopt a wait-and-see attitude and
delay investment and consumption. This would slow
economic activity, raising credit risks for financial insti-
tutions that could trigger an adverse macrofinancial
feedback loop.12 Third, macroeconomic uncertainty

12This channel originates from the “real options” literature,

where the option value of deferring decisions rises with uncertainty
(Bernanke 1983). Gilchrist, Sim, and Zakrajšek (2014), however,
find that uncertainty mainly affects investment by causing financial
distortions rather than through wait-and-see attitudes.

58

International Monetary Fund | October 2024

can affect macrofinancial stability through a credit
channel. This channel may arise because of reduced
credit supplied by financial intermediaries as they face
greater challenges in determining the creditworthiness
of new borrowers when the macroeconomic outlook is
more uncertain.13 These three channels can potentially
interact and mutually reinforce each other, amplifying
the effect of macroeconomic uncertainty on macrofi-
nancial stability.14

Recent technological innovations and social media

could aggravate uncertainty as well as its effect on
market tail risks. Over the past few years, investors
and depositors have been more attentive to surprises
in data and news disseminated through social media,
affecting their relationship with financial institutions
and intensifying stress episodes (see Online Annex
Figure 2.1.3). At the same time, fintech has made

13See, for example, Valencia (2017); Buch, Buchholz, and Tonzer

(2015); Wu and Suardi (2021); and Berger and others (2022).

14The literature has considered the effect of uncertainty both as
a shock and as a type of vulnerability that can amplify the effects
of other shocks. For example, Arellano, Bai, and Kehoe (2010);
Christiano, Motto, and Rostagno (2014); and Gilchrist, Sim, and
Zakrajšek (2014) analyze how increasing risk premiums can transmit
uncertainty shocks to economic outcomes, while He and Krish-
namurthy (2013) examine how high borrowing costs from higher
uncertainty can undermine firms’ profitability and solvency, making
them more vulnerable to financial shocks. Alfaro, Bloom, and Lin
(2022) show that uncertainty shocks and financial shocks to firms
can amplify each other.

CHAPTER 2  MACROFINANCIAL STABILITY AMId hIGh GLOBAL ECONOMIC uNCERTAINTY

conducting transactions faster and easier, exacerbating
funding and market liquidity risks (see the April 2022
Global Financial Stability Report). In addition, artificial
intelligence (AI) is penetrating deeply into the finan-
cial sector, with institutional investors’ use of AI-based
algorithmic trading strategies further raising market
volatility risks because of a potential increase in herd-
ing behavior among investors using similar AI models
(see Chapter 3). Although these innovations have
benefits, they also create systemic complexities that
can accelerate the transmission of shocks and amplify
the effect of macroeconomic uncertainty on financial
stability.

The Global Landscape of
Macroeconomic Uncertainty

Macroeconomic uncertainty can stem from differ-

ent sources. These include (1) innovations affect-
ing the real sector of the economy such as output,
product prices, factor costs, and firms’ profitability;
(2) monetary, fiscal, trade, and regulatory policies;
and (3) geopolitical tensions, for example, conflicts
or policy-driven decisions to impose barriers on
cross-border trade and capital flows.15 To quantify
macroeconomic uncertainty from these sources, the
chapter considers different measures. For example,
real sector uncertainty is proxied by two indica-
tors: the real economic uncertainty index (REU) of
Jurado, Ludvigson, and Ng (2015) and Ludvigson,
Ma, and Ng (2021), which reflects the accuracy of
forecasts for a large set of key macroeconomic vari-
ables, and by the dispersion in real GDP forecasts
based on Consensus Economics’ survey of profes-
sional forecasters. Uncertainty pertaining to domestic
macroeconomic and regulatory policies is captured
by the text-based economic policy uncertainty index
of Baker, Bloom, and Davis (2016), as well as by the
world uncertainty index of Ahir, Bloom, and Furceri
(2022). Finally, geopolitical uncertainty is measured

by the text-based geopolitical risk index of Caldara
and Iacoviello (2022).16

The different measures of macroeconomic uncer-
tainty tend to be positively but not strongly correlated.
The degree to which the macroeconomic uncertainty
measures correlate with each other varies considerably
but is generally modest (Figure 2.3, panel 1). This
suggests that the different measures capture some idio-
syncratic phenomena in addition to common shocks,
making it important to individually assess their associa-
tion with tail risks to economic activity.17 The REU is
of particular interest because it exhibits the strongest
correlation with other measures of macroeconomic
uncertainty, possibly because real sector outcomes may
capture uncertainty from different sources including
policies and geopolitics.

Measures of macroeconomic and financial uncer-
tainty may contain complementary information but
do not always fluctuate in tandem. The correlations
of macroeconomic uncertainty measures with com-
monly used measures of financial uncertainty—such
as that based on Ludvigson, Ma, and Ng (2021),
which captures the precision of forecasts for different
financial market variables, or the VIX—are generally
positive but also modest (Figure 2.3, panel 1). Look-
ing at the evolution of different measures of macro-
economic and financial uncertainty for the United
States, for example, makes evident that in some
major crises, such as the global financial crisis or the
COVID-19 pandemic, all measures spiked in tandem
(Figure 2.3, panel 2). However, for many economic
and political events, only some of the measures have
shown a significant response while other measures
have remained muted. The US dot-com bubble of
the late 1990s is one such case that is only captured
by measures of financial uncertainty (VIX), while the
intensification of trade tensions between China and
the United States that started around 2018 is largely
captured by an increase in the economic policy
uncertainty index.

15Several other factors could also influence macroeconomic

16See Online Annex 2.2 for further details on the various measures

uncertainty such as climate change, climate policy, and technological
innovation. These are not explicitly analyzed in the chapter because
of limited data availability. However, some of the measures of macro-
economic uncertainty used in the chapter may at least partly capture
the effects of these sources as well. There may also be some overlap
between the different sources of macroeconomic uncertainty—for
example, that stemming from geopolitical tensions could affect both
real outcomes and policies.

of uncertainty.

17Correlation among measures of uncertainty has increased
over time (Online Annex Figure 2.1.4). Kozeniauskas, Orlik, and
Veldkamp (2018) show that measures of economic uncertainty are
statistically distinct and can covary positively or negatively depend-
ing on the type of shock. Nevertheless, these measures tend to have
some common fluctuations beyond the business cycle effect, which is
driven by changes to macroeconomic volatility.

International Monetary Fund | October 2024

59

GLOBAL FINANCIAL STABILITY REPORT: STEAdYING ThE COuRSE: uNCERTAINTY, ARTIFICIAL INTELLIGENCE, ANd FINANCIAL STABILITY

Measures of macroeconomic and financial uncer-
tainty can remain disconnected for a period of time.
Commonly used measures of financial uncertainty are
limited in scope and generally restricted to certain time
horizons. For example, measures of option-implied
market volatility, such as the VIX, capture risk related
to stock market returns—that is, the performance of
publicly traded firms as perceived by investors—at
short time horizons. In contrast, the information
embedded in measures of macroeconomic uncertainty

such as policy uncertainty can be relevant for assessing
the outlook of an economy over much longer horizons,
for example, when it pertains to geopolitical shocks or
to electoral cycles.18 This may at least partly explain
why at times financial market volatility (realized and

18Policy uncertainty can increase because of electoral uncertainty

when candidates’ policy proposals diverge significantly, or there
is less clarity on the proposals. Goodell, McGee, and McGroarty
(2020) show that changes in the incumbent party’s probability of
reelection drive key changes to policy uncertainty.

60

International Monetary Fund | October 2024

AEsEMsGlobal EPU versus SP500 volatilityGlobal EPU versus VIXGlobal EPU versus MSCI World Index volatilityEPUGPRIREUVIXGulf War I9/11GulfWar IILehman Brotherscollapse US-Chinatrade warCOVID-19Dot-combubbleburstMacro-MarketDisconnectFINUGDPFEPUWUIVIXREUGPRIFigure 2.3. Measures of Macroeconomic and Financial UncertaintyMeasures of uncertainty are not perfectly correlated ...1. Correlations between Selected Measures of Macroeconomic andFinancial Uncertainty, 1990–2023(Correlation coeﬃcient)... and may increase idiosyncratically around certain events.... and is often observed in both advanced and emerging market economies.2. Selected Measures of Macroeconomic and Financial Uncertainty forthe United States, 1990–2024(Standardized values)−26024A disconnect between the level of macroeconomic and financial uncertainty can arise ...3. Global Economic Policy Uncertainty Relative to Realized and ImpliedStock Market Volatility, 1997–2024(Ratio, normalized)041234. Realized Stock Market Volatility and Economic Policy Uncertaintyacross Economies, 1990–2024(Standardized values)−46−2024Correlation−1.0−0.50.00.51.0Economic policy uncertaintyCountry-specific MSCI volatility−202468Sources: See Online Annex 2.1; and IMF staﬀ calculations.Note: Panel 1 shows the correlation between several macroeconomic and financial uncertainty measures from 1990 to 2023. Panel 2 depicts standardized measures of uncertainty for the United States. Panel 3 shows the monthly ratios of the global economic policy uncertainty index to the realized S&P 500 volatility, to the VIX, and to the MSCI World Index volatility, with the ratios normalized to have a mean of one over 1997:M1–2024:M2. Panel 4 shows a scatterplot of standardized country-specific economic policy uncertainty against MSCI country-specific realized stock market volatility. Realized stock market volatility is computed as the within-quarter volatility of daily returns. The fourth quadrant of the panel (with above average macroeconomic uncertainty and below average stock market volatility) indicates observations of a macro-market disconnect across countries. AEs = advanced economies; EMs = emerging markets; EPU = economic policy uncertainty index; FINU = financial uncertainty measure; GDPF = real GDP forecast dispersion; GPRI = geopolitical risk index; REU = real economic uncertainty index; SP500 = S&P 500; VIX = Chicago Board Options Exchange Volatility Index; WUI = world uncertainty index.1997200003060912151821241990949820020711151924CHAPTER 2  MACROFINANCIAL STABILITY AMId hIGh GLOBAL ECONOMIC uNCERTAINTY

implied) may be low while macroeconomic uncertainty
is high—that is, there is a “macro-market discon-
nect”—which may remain persistent (Figure 2.3,
panels 3 and 4).19

Macroeconomic Uncertainty and
Downside Risk to Output

To assess the association of macroeconomic uncer-
tainty with downside risks to future output, the chap-
ter estimates an augmented GaR model.20 The analysis
looks at the full distribution of future GDP growth
at different horizons, with a focus on the left tail (the
10th percentile) as a measure of downside tail risk.
The results show that an increase in macroeco-
nomic uncertainty is associated with a significant rise
in downside risk to future GDP growth. A one-stan-
dard-deviation increase in measures of macroeconomic
uncertainty reduces one-quarter-ahead real GDP
growth (annualized) by 0.5 to 2.0 percentage points
(Figure 2.4, panel 1).21 Measures of macroeconomic
uncertainty based on real outcomes such as the REU
and dispersion of GDP forecasts have quantitatively
the largest effect, but the association of all measures
with downside risks to future output is statistically
significant. Moreover, the impact of macroeconomic
uncertainty persists up to about seven quarters after
the shock (Figure 2.4, panel 2). In cumulative terms,

19For example, while the 2016 US presidential election was asso-
ciated with significant uncertainty about its effect on the country’s
long-term policies, volatility in stock markets remained low. Simi-
larly, Brexit caused considerable uncertainty about UK trade, growth,
and immigration policies, yet it had less of an impact on short-term
stock market volatility. Aït-Sahalia and others (2024) show that
macroeconomic uncertainty and stock return volatility could be
disconnected. In particular, the relationship between macroeconomic
uncertainty and volatility could vary over time depending on the
precision of political signals, even though (a priori) they are expected
to be positively correlated (Pástor and Veronesi 2013).

20The analysis extends the baseline GaR model of Adrian,
Boyarchenko, and Giannone (2019) to include measures of
macroeconomic and financial uncertainty, while controlling for
current GDP growth, financial conditions, and country fixed effects.
Estimations are carried out using panel quantile regressions for dif-
ferent time horizons. While the focus of the analysis is on predicting
downside risks to future output, and not on identifying the causal
effects of uncertainty on future output, potential endogeneity con-
cerns are addressed in robustness exercises. See Online Annex 2.3 for
more details on the estimation methodology, results, and robustness
analysis.

21These estimates are quantitatively significant, considering that
the annual output decline in the bottom 10th percentile of the his-
torical GDP growth distribution for the full sample, across advanced
and emerging market economies, is 1.2 percent.

an increase in the REU equivalent to that observed
on average across countries during the global financial
crisis translates into a decline in one-year-ahead GaR
of about 1.2 percentage points.22

Increased uncertainty has an asymmetric association
with the distribution of future GDP growth, affecting
downside (“bad”) tail risks more strongly than upside
(“good”) tail risks. Macroeconomic uncertainty has a
negligible effect on the median of the distribution of
future real GDP growth but a large and statistically
significant effect on the lower and upper quantiles
(Figure 2.4, panel 3).23 Overall, however, an increase
in uncertainty exerts a stronger effect on downside tail
risks to future GDP growth (at the 5th or 10th per-
centiles) than on upside tail risks (at the 90th or
95th percentiles). Additional analysis presented in
Online Annex 2.2 suggests that some technological
revolutions (for example, the 1990s dot-com bubble
in the United States and the mobile phone revolution
in Finland), postcrisis reforms (for example, those in
Korea after the Asian financial crisis), and major polit-
ical shifts (for example, the reunification of East and
West Germany in the late 1980s) could be considered
as examples of “good” uncertainty that raised upside
tail risks to future growth. By contrast, increased
macroeconomic uncertainty at the onset of the global
financial crisis or the COVID-19 pandemic are exam-
ples of “bad” uncertainty.24

Machine learning models can improve predictions
of downside risk to GDP growth, particularly when
measures of macroeconomic uncertainty are added as
predictors. Applying machine learning models—such
as panel quantile random forest and panel quantile
neural network—to the GaR framework (ML-GaR)

22Note that an increase in the financial uncertainty measure also
raises downside risks to real GDP growth beyond the effect of the
financial conditions index included in the model. These estimates
are based on panel data, capturing the cross-country average effects.
The effects of macroeconomic and financial uncertainty variables for
individual countries may be larger or smaller.

23The macroeconomic uncertainty measures are significantly
associated with the mean of one-quarter-ahead GDP growth when
the financial conditions indicator is not included in the model, but
they are not significant otherwise.

24Episodes of “bad” and “good” uncertainty are distinguished by

following the approach of Segal, Shaliastovich, and Yaron (2015)
(see Online Annex 2.2 for details). Intuitively, uncertainty marked
by positive shocks like technological advancements or unexpected
market opportunities can potentially be “good” because businesses
and investors, perceiving future gains, may increase investment and
expand operations, driving economic growth and higher asset prices.
By contrast, “bad” uncertainty, related to negative shocks to macro-
economic variables, would lower prices and reduce investment.

International Monetary Fund | October 2024

61

GLOBAL FINANCIAL STABILITY REPORT: STEAdYING ThE COuRSE: uNCERTAINTY, ARTIFICIAL INTELLIGENCE, ANd FINANCIAL STABILITY

Figure 2.4. Uncertainty and Downside Risks to Output: Results from Panel Quantile GaR Model

Measures of macroeconomic uncertainty have a
strong impact on downside risks to output ...
1. Effect of Measures of Uncertainty on
Growth-at-Risk, One Quarter Ahead
(Percentage points, annualized)

... and effects are persistent up to about seven
quarters ahead.

A rise in uncertainty has asymmetric effects on
the distribution of future GDP growth.

2. Effect of Real Economic Uncertainty on
Growth-at-Risk at Different Horizons
(Percentage points, annualized)

3. Effect of Uncertainty on Growth-at-Risk,

One Quarter Ahead
(Percentage points, annualized)

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3

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5

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8

9 10 11 12

Quarters

0.5

0

−0.5

−1.0

−1.5

−2.0

−2.5

4
3
2
1
0
−1
−2
−3
−4
−5
−6

Real economic uncertainty index
World uncertainty index
Financial uncertainty measure

5

20

35

50
Quantiles

65

80

95

Sources: Ahir, Bloom, and Furceri 2022; Baker, Bloom, and Davis 2016; EUROPACE AG/Haver Analytics; IMF, Global Data Source and International Financial Statistics
databases; LSEG Datastream; Organisation for Economic Co-operation and Development, Main Economic Indicators database; and IMF staff calculations.

Note: Panel 1 illustrates the impact of a one-standard-deviation increase in various measures of uncertainty individually on the 10th percentile of the one-quarter-ahead
real GDP growth distribution, with current real GDP growth, financial conditions, and country fixed effects controlled for. Panel 2 shows the impact of a
one-standard-deviation increase in the real economic uncertainty index used in the chapter, based on Ludvigson, Ma, and Ng (2021), on the 10th percentile of the average
future real GDP growth distribution at different time horizons. The solid line shows estimated coefficients; shaded areas around estimated coefficients correspond to
90 percent confidence intervals. Panel 3 depicts the effect of various measures of uncertainty on the full distribution of one-quarter-ahead real GDP growth. All estimations
are carried out using panel quantile regressions across the economies considered in the sample. Error bars indicate 90 percent confidence intervals. For more details on
variables, estimations, and data sources, see Online Annexes 2.1, 2.2, and 2.3. GaR = growth-at-risk.

improves out-of-sample prediction accuracy compared
to the standard benchmark GaR model based on
linear quantile regressions. Specifically, out-of-sample
prediction accuracy for advanced and emerging market
economies improves by up to 7 percent at different
horizons (Figure 2.5, panels 1 and 2, green bars).25
Adding measures of macroeconomic uncertainty (such
as the REU) as predictors further improves the out-of-
sample forecast performance of ML-GaR models by
5 to 13 percent relative to the standard GaR models
that exclude uncertainty (Figure 2.5, panels 1 and 2,
red bars).26

ML-GaR models also show that macroeconomic
uncertainty contributes at least as much as the finan-
cial conditions index to predicting downside risk to

25The benchmark quantile regression GaR model and the machine

learning models are estimated on the same panel of economies and
predict the 10th percentile of the one- or four-quarter-ahead GDP
growth distribution. See Online Annex 2.3 for further details.

26These results are qualitatively robust to the use of alternative

measures of macroeconomic uncertainty and training samples
that exclude major crises such as the global financial crisis and the
COVID-19 pandemic (see Online Annex 2.3 for details).

real GDP growth. For both one- and four-quarter-
ahead forecasts of downside risks to output growth in
advanced and emerging market economies, the REU
on average contributes more to predictions than the
financial conditions index typically included in GaR
models (Figure 2.5, panels 3 and 4).27

Market and credit channels play an important role in

transmitting the effect of macroeconomic uncertainty
on future output growth. An increase in macroeco-
nomic uncertainty is associated with a greater likelihood
of large negative realizations of stock market returns, as
well as of spikes in sovereign bond spreads (Box 2.1).
Furthermore, macroeconomic uncertainty influences
tail risks to future bank lending, particularly in coun-
tries where banking exposure to sovereign debt is high.

27Contributions for each variable to the forecast are calculated as
average absolute Shapley values in panel quantile random forest and
quantile neural network models. These contributions may vary across
countries and over time. For example, the financial conditions index
may play a more important role than the REU during periods of
financial stress in advanced economies. On average, the contribution
of the REU is higher when the quantile neural network models,
which provide more flexibility to capture complex nonlinearities
between uncertainty and other predictors, are used.

62

International Monetary Fund | October 2024

CHAPTER 2  MACROFINANCIAL STABILITY AMId hIGh GLOBAL ECONOMIC uNCERTAINTY

Figure 2.5. Improvement in Predictive Accuracy from Applying Machine Learning GaR Models

Machine learning tools increase the predictive power of growth-at-risk
models, especially when macroeconomic uncertainty is considered ...

... and this holds for diﬀerent time horizons.

1. Machine Learning GaR: One-Quarter-Ahead Out-of-Sample

2. Machine Learning GaR: Four-Quarters-Ahead Out-of-Sample

Growth-at-Risk Forecasts
(Accuracy change relative to benchmark model, percent)

Growth-at-Risk Forecasts
(Accuracy change relative to benchmark model, percent)

15

12

9

6

3

0

Excluding real economic uncertainty
Including real economic uncertainty

Excluding real economic uncertainty
Including real economic uncertainty

EMs
AEs
Quantile random forest

EMs
AEs
Quantile neural network

EMs
AEs
Quantile random forest

EMs
AEs
Quantile neural network

15

12

9

6

3

0

Macroeconomic uncertainty is an important predictor ...

... particularly in emerging markets.

3. Predictive Importance of Macroeconomic Uncertainty and Financial

4. Predictive Importance of Macroeconomic Uncertainty and Financial

Conditions, One-Quarter-Ahead Out-of-Sample Growth-at-Risk Forecasts
(Average absolute Shapley value, percent)

Conditions, Four-Quarters-Ahead Out-of-Sample Growth-at-Risk Forecasts
(Average absolute Shapley value, percent)

2.0

1.5

1.0

0.5

0

Financial conditions index
Real economic uncertainty index

Financial conditions index
Real economic uncertainty index

EMs
AEs
Quantile random forest

EMs
AEs
Quantile neural network

EMs
AEs
Quantile random forest

EMs
AEs
Quantile neural network

2.0

1.5

1.0

0.5

0

Sources: EUROPACE AG/Haver Analytics; IMF, Global Data Source and International Financial Statistics databases; Organisation for Economic Cooperation and
Development, Main Economic Indicators database; LSEG Datastream; and IMF staﬀ calculations.

Note: Panels 1 and 2 compare the predictive accuracy of alternative ML-GaR models (quantile random forest and quantile neural network) against a benchmark GaR model
estimated using panel quantile regressions for one- and four-quarter-ahead output growth (at the 10th percentile), respectively, for a sample of advanced and emerging
market economies. The benchmark GaR model includes current real GDP growth, the financial conditions index used in the chapter, and country fixed eﬀects as predictors.
The comparator ML models include the same variables for results presented in green bars and additionally include the real economic uncertainty index for results
presented in red bars. The predictive accuracy improvement is defined as one minus the percentage change in realized quantile loss for the 10th percentile when moving
from benchmark GaR to ML-GaR predictions for out-of-sample forecasts. Panels 3 and 4 display the average absolute Shapley values for the real economic uncertainty
index and the financial conditions index to assess the importance of each variable for one- and four-quarter-ahead predictions of GaR (10th percentile of output growth).
For additional details, see Online Annex 2.3. AEs = advanced economies; EMs = emerging markets; GaR = growth-at-risk; ML = machine learning.

Macroeconomic Uncertainty Amplifies
the Impact of Macrofinancial
Vulnerabilities

Macroeconomic uncertainty interacts with debt
vulnerabilities to exacerbate downside tail risks to
GDP growth. High real economic uncertainty com-
bined with excessive domestic credit (measured as the
deviation of the credit to the private-sector-to-GDP

ratio from its long-term trend) reduces one-quarter-
ahead downside tail risk to GDP growth (that is, the
10th percentile of the distribution of future GDP
growth) by 0.6 percentage points (Figure 2.6, panel 1).
Similarly, high public debt levels (captured by the
deviation of the public-debt-to-GDP ratio from its
long-term trend) significantly increase downside risks
to GDP growth, particularly when real economic

International Monetary Fund | October 2024

63

GLOBAL FINANCIAL STABILITY REPORT: STEAdYING ThE COuRSE: uNCERTAINTY, ARTIFICIAL INTELLIGENCE, ANd FINANCIAL STABILITY

64

International Monetary Fund | October 2024

Baseline eﬀect of public debtInteraction eﬀect with high uncertaintyBaseline eﬀect of creditEﬀect of easing in financial conditions (under high uncertainty)Eﬀect of easing in financial conditions (under low uncertainty)Eﬀect of easing in financial conditions (with large disconnect)Eﬀect of easing in financial conditions (with small disconnect)FCI easing without MPP (under large disconnect)FCI easing with MPP (under large disconnect)Figure 2.6. Macroeconomic Uncertainty and Macrofinancial VulnerabilitiesHigh macroeconomic uncertainty amplifies the eﬀects of macrofinancial vulnerabilities on downside risks to future GDP growth ...1. Eﬀect on GaR of Interaction between Real Economic Uncertainty andPublic and Private Sector Leverage(Percentage points, one quarter ahead)−2.00−1.5−1.0−0.5−13012... as well as the intertemporal trade-oﬀ posed by easy financial conditions on growth-at-risk ...2. Eﬀect of Easing in Financial Conditions on GaR amid High and LowReal Economic Uncertainty(Percentage points)Quarters123456789101112Eﬀect of credit gapEﬀect of public debt gap... particularly when uncertainty in the macroeconomy becomes substantially disconnected from that in financial markets.3. Eﬀect of Easing in Financial Conditions on GaR amid High and LowMacro-Market Disconnect(Percentage points)−1.52.5−1.0−0.500.51.01.52.0Quarters121234567891011Macroprudential policies can help mitigate the intertemporal trade-oﬀ and maintain financial stability.4. Eﬀect of Easing in Financial Conditions on GaR with and withoutMacroprudential Policies(Percentage points)−0.61.5−0.300.30.60.91.2Quarters112234567891011Sources: EUROPACE AG/Haver Analytics; IMF, Global Data Source, Integrated Macroprudential Policy, and International Financial Statistics databases; LSEG Datastream; Organisation for Economic Co-operation and Development, Main Economic Indicators database; and IMF staﬀ calculations.Note: Panel 1 shows the impact of private credit-to-GDP gap and public debt-to-GDP gap on GaR (10th percentile of the distribution of one-quarter-ahead output, annualized) amid high real economic uncertainty. The analysis extends the baseline GaR model of Adrian, Boyarchenko, and Giannone (2019) by adding variables relating to the gaps in private credit and public debt, a dummy variable indicating periods of high uncertainty, and their respective interaction terms. Periods of high (low) uncertainty are defined as those with values above (below) the median. The credit-to-GDP gap and total public debt-to-GDP gap are measured by the deviations of the variables from a one-sided Hodrick-Prescott filter. Estimations are conducted using panel quantile regressions for the countries in the sample, depending on data availability. Error bars (solid color) indicate 90 percent confidence intervals for the sum of the coeﬃcients on vulnerabilities and their interaction with the uncertainty dummy variable. Panels 2 and 3 present the impact of a one-standard-deviation easing in financial conditions on the term structure of GaR amid high (low) real economic uncertainty and amid high (low) macro-market disconnect, respectively. High (low) macro-market disconnect is defined as the ratio of real economic uncertainty to realized market volatility that is above (below) the mean. Panel 4 illustrates the impact of a one-standard-deviation easing in financial conditions during a period of macroprudential tightening, using a methodology similar to that in Chapter 2 of the April 2021 Global Financial Stability Report. The macroprudential tightening regime refers to quarters in the preceding year with net macroprudential tightening. See Online Annexes 2.1 and 2.4 for further details on estimations. Solid lines show estimated coeﬃcients; shaded areas around estimated coeﬃcients correspond to 90 percent confidence intervals. FCI = financial condition index; GaR = growth-at-risk; MPP = macroprudential policies.CHAPTER 2  MACROFINANCIAL STABILITY AMId hIGh GLOBAL ECONOMIC uNCERTAINTY

uncertainty is high instead of low—possibly through
the effects of increased public debt on borrowing costs,
and thereby on investment and consumption.28

More generally, high macroeconomic uncertainty
can exacerbate macrofinancial stability risks associated
with loose financial conditions. In the standard GaR
framework, changes in financial conditions lead to
intertemporal trade-offs. In the short term, an easing
of financial conditions that is typically associated with
rising asset valuations and a compression of credit
spreads and stock market volatility reduces downside
tail risks to GDP growth. Easy financial conditions,
however, also encourage a buildup of debt vulnera-
bilities which exacerbate downside tail risks to GDP
growth in the medium term. This intertemporal trade-
off has relevance for monetary and macroprudential
policy making because tighter policies can help weaken
it (Adrian and Liang 2018). An increase in macroeco-
nomic uncertainty can, however, amplify the trade-off,
particularly when it is not synced with financial market
volatility—that is, when there is a macro-market
disconnect. This is because such a disconnect increases
the possibility of sudden jumps in financial market vol-
atility and market crashes in the face of adverse shocks
as investors realign their expectations, with broader
implications for financial stability.29

The analysis suggests that macroeconomic uncer-
tainty significantly influences the intertemporal trade-
off. Estimates of the GaR model augmented with the
REU show that under high macroeconomic uncer-
tainty, looser financial conditions exacerbate down-
side tail risks to GDP growth in the medium term
(Figure 2.6, panel 2). The impact of looser financial
conditions is even more pronounced when there is
a large macro-market disconnect, confirming that
compressed market volatility may reverse quickly in the

28For example, uncertainty about future economic policies can
prompt investors to demand higher risk premiums, thereby increas-
ing government borrowing costs and making debt servicing more
expensive. Machine learning models further expose the nonlinear
relationship between public and private debt and downside risks to
GDP growth, conditional on the level of real economic uncertainty
(Online Annex Figure 2.4.1).

29Periods of compressed risk premiums may be associated with
overexuberant sentiment and are often followed by a reversal in val-
uations (Greenwood and Hanson 2013). Extending the GaR model
with a variable for a large macro-market disconnect—defined as the
ratio of real economic uncertainty to realized stock market volatility
that is above its mean level—confirms that such a disconnect is asso-
ciated with an increase in downside tail risks to future output growth
(see Online Annex 2.4).

face of a shock when real economic uncertainty is high
(Figure 2.6, panel 3).

Macroprudential policies can help mitigate the
intertemporal trade-off. Further analysis suggests that
a net tightening of macroprudential policies can help
offset the rise in medium-term downside risks that
accompany easy financial conditions, especially when
there is a macro-market disconnect.30 Specifically,
when the macro-market disconnect is large, a loosen-
ing of financial conditions coupled with a net tight-
ening of macroprudential policies is associated with a
reduction in downside risks to GDP growth of 0.3 to
0.6 percentage points in the medium to long terms,
compared with a scenario in which no macropruden-
tial measures are put in place (Figure 2.6, panel 4).
These findings suggest that policymakers may need to
be more proactive in deploying policies aimed at pre-
serving financial stability in periods when macroeco-
nomic uncertainty is high relative to market volatility.
More generally, credible policy frameworks may also
help reduce macroeconomic uncertainty and its impact
on downside risks to output (Box 2.2).

The Downside Risks of Macroeconomic
Uncertainty and Cross-Border Spillovers

The downside risks from macroeconomic uncer-
tainty can spill over across borders through trade and
financial linkages. An increase in macroeconomic
uncertainty in a country’s major trading and financial
partners can raise domestic downside risks through
the real channel (as the demand for the country’s
exports dwindles) as well as through the market and
credit channels (by limiting foreign capital flows).31
To investigate these possible cross-border spillover

30This result complements the findings in Chapter 2 of the April

2021 Global Financial Stability Report, which show that macro-
prudential policy can temper buildups in private sector leverage,
reducing downside risks to growth in the medium term. The analysis
here considers the presence of all types of macroprudential measures,
focusing on the frequency of tightening episodes rather than on their
intensity (for details, see Online Annex 2.5).

31A large body of literature examines the cross-border spillover
effects of global financial uncertainty (proxied by the VIX) on asset
prices, capital flows, domestic credit growth, and output, documenting
strong effects (for example, Rey 2013; Obstfeld, Ostry, and Qureshi
2019; Bhattarai, Chatterjee, and Park 2020). Biljanovska, Grigoli, and
Hengge (2021) show that an increase in economic policy uncertainty
in China, Europe, and the United States reduces economic activity in
the rest of the world, and Londono, Ma, and Wilson (2024) find that
foreign uncertainty negatively affects domestic economic outcomes.
These studies, however, mostly focus on the mean spillover effects of
uncertainty and not on tail risks to output.

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GLOBAL FINANCIAL STABILITY REPORT: STEAdYING ThE COuRSE: uNCERTAINTY, ARTIFICIAL INTELLIGENCE, ANd FINANCIAL STABILITY

Figure 2.7. Cross-Border Spillover Eﬀects of Foreign Uncertainty

An increase in real economic uncertainty in a country’s major trade and financial partners can raise downside risks to domestic output.

1. Eﬀect of an Increase in Foreign Real

Economic Uncertainty (Weighted by Trade)
on GaR
(Percentage points, annualized)

2. Eﬀect of an Increase in Foreign Real
Economic Uncertainty (Weighted by
Banking Exposures) on GaR
(Percentage points, annualized)

3. Eﬀect of an Increase in Foreign Real
Economic Uncertainty (Weighted by
Portfolio Exposures) on GaR
(Percentage points, annualized)

1.0

0.5

0

−0.5

−1.0

−1.5

−2.0

−2.5

−3.0

1.0

0.5

0

−0.5

−1.0

−1.5

−2.0

−2.5

−3.0

1.0

0.5

0

−0.5

−1.0

−1.5

−2.0

−2.5

−3.0

1

2

3

4

5

8

9 10 11

12

7

6
Quarters

1

2

3

4

5

6

7

8

9 10 11

12

1

2

3

4

5

6

7

8

9 10 11

12

Quarters

Quarters

Sources: EUROPACE AG/Haver Analytics; IMF, Global Data Source and International Financial Statistics databases; LSEG Datastream; Organisation for Economic
Co-operation and Development, Main Economic Indicators database; and IMF staﬀ calculations.

Note: The figure shows the eﬀect of a one-standard-deviation increase in various measures of foreign uncertainty on the 10th percentile of future quarterly domestic real
GDP growth (annualized) over time. In panel 1, the measure of foreign uncertainty is calculated as a weighted average of the chapter’s real economic uncertainty index in a
country’s trading partners, with weights computed as the sum of the ratios of bilateral exports and imports to domestic GDP. In panel 2 (3), the measure of foreign
uncertainty is calculated as a weighted average of the chapter’s real economic uncertainty index in a country’s financial partners, with weights computed as the sum of the
ratios of bilateral banking (portfolio) assets and liabilities to domestic GDP. The model controls for current GDP growth, the domestic financial conditions index, measures
of domestic real economic and financial uncertainty (based on Ludvigson, Ma, and Ng 2021), global real GDP growth, the global financial conditions index, a dummy
variable for the global financial crisis and country fixed eﬀects. Estimations are conducted using a panel quantile regression framework for the full sample of advanced and
emerging market economies. For more details on computations and data sources, see Online Annex 2.6. Graph lines represent estimated coeﬃcients; shaded areas around
estimated coeﬃcients correspond to 90 percent confidence intervals. GaR = growth-at-risk.

effects, the chapter constructs measures of “foreign
uncertainty” as a weighted average of measures of
macroeconomic uncertainty in each country’s major
trading and financial partners—with weights based
on the intensity of trade (export and import) or
banking and portfolio investment exposures between
the domestic and partner economy. The foreign
uncertainty measures are included in the GaR model,
along with measures of domestic uncertainty and
other relevant control variables, and the model is esti-
mated for the full sample of advanced and emerging
market economies.32

The findings show that foreign macroeconomic

uncertainty can exacerbate downside tail risks to
domestic GDP growth through both trade and
financial linkages. The 10th percentile of the one-
quarter-ahead distribution of GDP growth declines

32See Online Annex 2.6 for methodological details and detailed

results of the cross-border spillover analysis.

66

International Monetary Fund | October 2024

by 1.7 percentage points following a one-stan-
dard-deviation increase in the trade-weighted
foreign macroeconomic uncertainty measures (based
on the REU) (Figure 2.7, panel 1). The effect is,
however, less persistent than that resulting from
a similar increase in domestic macroeconomic
uncertainty (Figure 2.3, panel 2) and peters out in
about three quarters. Similar results are obtained
for an increase in the REU in partner countries
with which a country has strong banking relation-
ships (Figure 2.7, panel 2) or cross-border portfolio
investment exposures (Figure 2.7, panel 3).33 In
the latter case, the effect is notably more persistent,
suggesting that nonbank financial intermediaries can
potentially play an important role in transmitting

33These results are robust to other measures of macroeconomic
uncertainty. For uncertainty related to foreign economic policy, the
impact on domestic downside risks to output is found to be more
persistent, lasting up to six quarters.

CHAPTER 2  MACROFINANCIAL STABILITY AMId hIGh GLOBAL ECONOMIC uNCERTAINTY

macroeconomic uncertainty across borders through
the market channel.34

International reserve buffers and exchange rate

flexibility can help mitigate the adverse implications of
foreign uncertainty. While macroeconomic uncertainty
can be a potential source of international financial
contagion, additional analysis shows that downside
risks arising from a rise in foreign macroeconomic
uncertainty could be mitigated by building adequate
international reserve buffers, or through greater
exchange rate flexibility (Online Annex 2.6).

Conclusion and Policy Recommendations

Macroeconomic uncertainty remains elevated globally

since the COVID-19 pandemic. This chapter shows
that high uncertainty about economic fundamentals
and policies increases downside risks to future real GDP
growth, stock and bond market returns, and bank lend-
ing. Macrofinancial vulnerabilities, such as high ratios of
public and private sector debt to GDP, can interact with
high macroeconomic uncertainty to amplify the effects
of adverse shocks on future output growth. Moreover,
high macroeconomic uncertainty worsens the intertem-
poral trade-off posed by an easing of financial condi-
tions for downside risk to medium-term output growth,
particularly when accompanied by low financial market
volatility (a macro-market disconnect). The impact of
macroeconomic uncertainty tends to spill over across
borders through trade and financial linkages, raising
the risk of international contagion in the face of large
adverse shocks. The chapter also presents evidence that
macroprudential policies, larger buffers of international
reserves, and enhanced exchange rate flexibility can help
mitigate the domestic and cross-border effects of macro-
economic uncertainty.

The findings also show that machine learning models

can improve the forecasting capacity of systemic risk
assessment frameworks such as the GaR framework. Reg-
ulatory and policy institutions can enhance their systemic
risk monitoring frameworks by explicitly considering the
role of macroeconomic uncertainty as a key determinant
of systemic risk while also exploiting the advantages of
AI tools such as machine learning models for predict-
ing downside tail risks to output and financial markets.
Other AI tools (such as natural language models) can be

34Studies note that nonbank financial intermediaries are more
sensitive to changes in drivers of global liquidity flows than banks
(Buch and Goldberg 2024) and that nonbanks’ large-scale selling
during uncertain times can amplify global market disruptions (Ma,
Xiao, and Zeng 2022).

used to extract useful and high-frequency information
from a range of text-based sources (including firms’ earn-
ing call reports, social media, and local and global news)
to enhance real-time monitoring of systemic risk.35

Policy actions should focus on reducing macroeco-
nomic uncertainty as well as on mitigating its adverse
effects by strengthening resilience and containing
macrofinancial vulnerabilities.

Reducing Policy Uncertainty

Credible monetary and fiscal policy frameworks and
improved communication can reduce macroeconomic
uncertainty and its adverse effects on the economy.
Enhancing the credibility of policy frameworks through,
for example, the adoption of fiscal and monetary policy
rules supported by strong institutions can reduce policy
uncertainty (Box 2.2). Credible policy frameworks can
also offer policymakers more room to cope with large
adverse shocks, thereby mitigating the effects of increases
in macroeconomic uncertainty on downside risks to
output growth. In addition, improved transparency and
well-designed policy communication frameworks can
steer market expectations and make policy decisions
more predictable and less uncertain.36

A stable financial regulatory framework is important

to help mitigate policy uncertainty. In the financial
sector, constant innovations open new loopholes and
sources of complexity, which can threaten financial
stability and require a regulatory response without delay.
To prevent these reforms from generating unnecessary
policy uncertainty among market participants and finan-
cial institutions, they should be announced and imple-
mented with clear communication strategies, robust
calibration, phase-in periods as necessary, and clear and
practical use of supervisory discretion and enforcement.

35Although machine learning methods are well suited for improving

systemic risk surveillance, their application entails conceptual and
practical challenges. First, data requirements and technological know-
how for applying machine learning tools are significant, which may
pose challenges for many emerging market and developing economies
with data, skill, and technological constraints. Second, weak signal-
to-noise ratios of financial variables can lead large and sophisticated
models to perform poorly out of sample. Finally, machine learning
methods often suffer from poor transparency and interpretability.
Online Annexes 2.3 and 2.4 discuss how these shortcomings have
been addressed in the chapter using cross-validation methods for
model selection and overfit mitigation, as well as by numerical simula-
tions and analysis of variable importance.

36For example, Blinder and others (2008) show that increased trans-

parency and improved policy communication among central banks
in major economies has increased the predictability of central banks’
interest rate decisions, reducing their impact on market volatility.

International Monetary Fund | October 2024

67

GLOBAL FINANCIAL STABILITY REPORT: STEAdYING ThE COuRSE: uNCERTAINTY, ARTIFICIAL INTELLIGENCE, ANd FINANCIAL STABILITY

Mitigating the Financial Stability Risks
Associated with Macroeconomic Uncertainty

Policymakers should deploy adequate macropru-
dential and fiscal policies to contain financial stability
risks arising from elevated macrofinancial vulnera-
bilities amid high macroeconomic uncertainty. As
high uncertainty can exacerbate the adverse effects of
macrofinancial vulnerabilities such as excessive private
sector leverage on the real economy, policymakers
should remain vigilant and proactively deploy macro-
prudential policies to limit these vulnerabilities. This
is particularly relevant when financial conditions are
loose and seemingly disconnected from the elevated
uncertainty prevailing in the broader economy.37 In
such cases, the response could include not only tighter
macroprudential policies but also a tighter monetary
policy stance by the central bank if that is aligned with
its goal of maintaining price stability (Adrian 2020).38

37The specific type of macroprudential policy to be deployed
would depend on prevailing circumstances and vulnerabilities. Poli-
cies to build resilience against turns in the financial and credit cycle,
such as countercyclical capital buffers, could be relevant. Borrow-
er-based measures could also be activated, for example, if lax finan-
cial conditions amid high uncertainty (a macro-market disconnect)
encourage excessive borrowing for investment in real estate.

38Higher uncertainty tends to shorten the horizons at which the
intertemporal trade-off becomes unfavorable for financial stability
(that is, the horizons at which downside risks to future growth rise in
response to an easing of current financial conditions), as Figure 2.6
illustrates. Because monetary and macroprudential policies can have
very different implementation and transmission lags, the level of
macroeconomic uncertainty and its disconnect from financial market
volatility can inform policymakers’ decisions regarding policy instru-
ments and the magnitude of the response.

In addition, fiscal policies should prioritize debt
sustainability to contain the adverse effects of elevated
public debt levels on borrowing costs that risk under-
mining macrofinancial stability (see the October 2024
Fiscal Monitor).

Prudential regulators and supervisors should

ensure that bank and nonbank financial institutions
assess their vulnerabilities to cross-border spillovers
of spikes in foreign macroeconomic uncertainty. At
the country level, given the cross-border spillovers of
macroeconomic uncertainty, adequate reserve buffers
and greater exchange rate flexibility can help coun-
tries cushion the potential adverse impacts of foreign
uncertainty shocks.

Amid rising geopolitical uncertainty, governments

should build adequate safety nets to mitigate mac-
rofinancial stability risks. As uncertainty related to
geopolitical developments can exacerbate tail risks to
domestic markets, credit, and output, governments
should make utmost efforts to reduce geopolitical
tensions through diplomacy and multilateral coop-
eration. To the extent that such cooperation remains
elusive, policymakers should devote resources to
identifying, quantifying, managing, and mitigating
financial stability risks associated with increases in
geopolitical tensions and uncertainty (see Chapter 3
of the April 2023 Global Financial Stability Report).
In this context, policymakers should ensure an
adequate level of international reserves as well as of
capital and liquidity buffers at financial institutions
to mitigate the adverse consequences of increasing
geopolitical risks.

68

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CHAPTER 2  MACROFINANCIAL STABILITY AMId hIGh GLOBAL ECONOMIC uNCERTAINTY

Box 2.1. Market and Credit Channels Are Important in Transmitting the Effect of
Macroeconomic Uncertainty on Output

Dynamics originating in the financial sector amid
high macroeconomic uncertainty can play a crucial
role in generating risks to macrofinancial stability
through the market and credit channels (Figure 2.2).
This box examines whether different measures of
macroeconomic uncertainty can help explain downside
tail risks to future asset (stock and sovereign bond)
returns as well as to bank lending by using a panel
quantile regression framework.1

Market Channel

The results indicate that macroeconomic uncertainty
raises the likelihood of future spikes in sovereign bond
yields. A one-standard-deviation increase in the real eco-
nomic uncertainty index (REU) is, on average, associated
with an increase of 150 basis points in upside tail risks to
sovereign bond spreads (defined as the 90th percentile of
the distribution of sovereign bond spreads) in emerging
market economies at a six-month horizon (Figure 2.1.1,
panel 1). By contrast, for the average advanced economy,
a shock of a similar magnitude to the REU increases
upside tail risks by about 25 basis points.2

In addition, the impact of macroeconomic uncer-
tainty on sovereign bond spreads is more pronounced
when fiscal vulnerabilities such as public debt service
and banks’ exposure to public debt are high rather than
low in emerging market economies (Figure 2.1.1, panel
2).3 In turn, a widening of sovereign bond spreads is
more likely to have an adverse impact on public debt
dynamics and macrofinancial stability (through the
sovereign–financial sector nexus) when public debt
or debt service as a share of GDP is already high (see
Chapter 2 of the April 2022 Global Financial Stability
Report). Thus, amid elevated public debt vulnerabilities

1See Online Annex 2.5 for methodological details and regres-

sion results for the analysis presented in this box.

2These findings complement the analysis in Chapter 1 of the

October 2024 Fiscal Monitor, which shows that higher global
policy uncertainty and geopolitical risk raise sovereign yield
volatility across countries.

3For advanced economies, fiscal vulnerabilities do not appear
to play a statistically significant role in amplifying the effect of
macroeconomic uncertainty on sovereign bond spreads (Box
Figure 2.1.1, panel 2). This could be because spikes in macroeco-
nomic uncertainty may trigger flight to quality effects, whereby
investors reallocate investments from equity or corporate bonds
with low credit ratings to sovereign bonds. In addition, in
response to increased macroeconomic uncertainty, central banks
in advanced economies are more likely to intervene in sovereign
bond markets (purchasing bonds to lower yields) when fiscal
vulnerabilities and yields are already elevated—reinforcing the
role of such bonds as a “safe haven” for investors.

and macroeconomic uncertainty, a sharp widening of
spreads becomes more likely as well as more damaging
to the economy (Figure 2.6, panel 1).

Analysis for stock market returns suggests that

increased macroeconomic uncertainty can also
exacerbate the risk of stock market crashes. A
one-standard-deviation increase in the REU can raise
downside tail risks to stock market returns (the 10th
percentile of the distribution of stock market returns)
by about 30 percentage points, one year after the
shock in advanced and emerging market economies
(Figure 2.1.1, panel 3).

Credit Channel

To examine the relevance of the bank lending channel,
the analysis estimates the effect of macroeconomic uncer-
tainty on tail risks to bank lending, defined as the 10th
percentile of future bank lending distribution. Macroeco-
nomic uncertainty is measured using the various indica-
tors discussed earlier as well as a new text-based measure
developed from transcripts of individual banks’ earnings
calls.4 The bank-level measure is intended to capture
more directly the level of uncertainty banks perceive,
which can influence their lending decisions.

The results indicate a strong relationship between
macroeconomic uncertainty and tail risks to future bank
lending. For example, a one-standard-deviation increase
in the REU is associated with a decline of about 1
percentage point (annualized) in the 10th percentile of
the distribution of one-quarter-ahead real credit growth
(Figure 2.1.2, panel 1).5 This effect persists through
about seven quarters, although it becomes smaller over
time. These findings are qualitatively robust across the

4Following the approach of Soto (2021), the bank-level uncer-
tainty measure is constructed by calculating the percentage of sen-
tences including words related to uncertainty in the earnings call
transcripts of specific banks, using definitions from the February
2024 update of the Loughran-McDonald Master Dictionary
(https://sraf.nd.edu/loughranmcdonald-master-dictionary/). These
percentages are then averaged across banks in a specific country to
arrive at a country-level indicator. As Online Annex 2.5 shows, the
bank-level text-based measure generally exhibits a low degree of
correlation with the other measures of uncertainty.

5The estimates are based on panel quantile regressions for a sam-
ple of 18 advanced and 13 emerging market economies using data
from 2001 to 2023. The model controls for lagged credit growth
dynamics, output growth, financial conditions, and financial vulner-
abilities. The last of these are measured by the position in the credit
cycle and banking sector fundamentals such as capital adequacy,
asset quality, profitability, and exposure to sovereign risk. The model
also includes country and time fixed effects. See Online Annex 2.5
for more details on the methodology and estimation results.

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69

GLOBAL FINANCIAL STABILITY REPORT: STEAdYING ThE COuRSE: uNCERTAINTY, ARTIFICIAL INTELLIGENCE, ANd FINANCIAL STABILITY

Box 2.1 (continued)

various measures of uncertainty, including the bank-
level text-based measure (Figure 2.1.2, panel 2), and
also hold for the subsamples of advanced and emerging
market economies.6

6Although the bank-level text-based measure of uncertainty

has a somewhat smaller impact on future tail risks to bank
lending than the REU, it remains statistically significant when all
other measures of uncertainty are included in the regression. This
suggests that the measure captures aspects of uncertainty that
may not be captured by other broad-based indicators.

Existing financial vulnerabilities can amplify the
effects of higher uncertainty on downside risks to bank
lending. Extending the model to include interaction
terms of the macroeconomic uncertainty measure
with financial vulnerabilities such as banks’ exposure
to sovereign debt shows that countries with a higher
bank exposure to sovereign risk exhibit a greater like-
lihood of a sharp decline in future bank loan growth
when macroeconomic uncertainty rises (Figure 2.1.2,
panel 3). For instance, a one-standard-deviation

70

International Monetary Fund | October 2024

Low vulnerabilityHigh vulnerabilityEMsAEsEMsAEsFigure 2.1.1. Macroeconomic Uncertainty and Tail Risks in Financial MarketsMacroeconomic uncertainty raises upside tail risks of sovereign bond spreads ... 1. Real Economic Uncertainty andUpside Tail Risk to SovereignSpreads(Basis points)−500250501001502000400100200300... particularly amid elevated fiscal vulnerabilities ...2. Real Economic Uncertainty, FiscalVulnerabilities, and Upside TailRisk to Sovereign Spreads, ThreeMonths Ahead(Basis points)... while raising downside tail risks to stock market returns.3. Real Economic Uncertainty andDownside Tail Risk to StockMarket Returns(Percentage points, returns)Sources: EUROPACE AG/Haver Analytics; Federal Reserve Bank of St. Louis, Federal Reserve Economic Data; ICE Bank of America; IMF, Global Data Source and International Financial Statistics databases; LSEG Datastream; Organisation for Economic Co-operation and Development, Main Economic Indicators database; and IMF staff calculations.Note: Panel 1 shows the effects of a one-standard-deviation increase in the real economic uncertainty index at time t on upside tail risks to sovereign bond spreads (that is, the 90th percentile of the distribution of changes to sovereign bond spreads between time t and time t + h) at different horizons (h = 1, 3, 6, and 12 months), estimated using panel quantile regressions with country fixed effects. The sample consists of monthly data for 20 advanced and 9 emerging market economies from 1990:M1 to 2023:M12. The underlying regressions include relevant controls for the sovereign bond market at the country and global levels, following Gilchrist and others (2022), as well as the lagged dependent variable. Spreads for each country are calculated relative to a benchmark economy (Germany for Euro area countries and the United States for all others) using only debt denominated in the same currency as that of the benchmark economy. Panel 2 illustrates the effect of a one-standard-deviation increase in the real economic uncertainty index on upside tail risks to sovereign bond spreads at a three-month horizon conditional on high and low levels of government debt service (as a percentage of GDP) and domestic banks’ exposure to sovereign risk (measured by domestic banks’ holdings of sovereign bonds as a share of their total assets). Low and high levels of government debt service and banks’ sovereign exposure are defined as the 25th percentile (green bars) and the 75th percentile (red bars) of the respective distributions. Note that the difference between green and red bars (that is, low and high fiscal vulnerabilities) is not statistically significant for advanced economies in the analysis. Panel 3 presents the effect of a one-standard-deviation increase in the real economic uncertainty index in month t on the 10th percentile of the distribution of the overall stock market return at different horizons (t + 1, 3, 6, and 12 months), estimated using panel quantile regressions with country fixed effects. The sample consists of monthly data for 21 advanced economies and 19 emerging markets from 1990:M1 to 2023:M12. The panel quantile regressions include as explanatory variables country fixed effects, lagged returns, and relevant controls at the country level, following Schmeling (2009) and Goyal and Welch (2008). Solid lines show estimated effects; shaded areas represent 95 percent confidence intervals. See Online Annex 2.5 for further details. AEs = advanced economies; EMs = emerging markets.1 month3 months6 months12 months1 month3 months6 months12 monthsBanks’ sovereignexposureAEsEMsAEsEMsGovernmentdebt service−70−60−50−40−30−20−10010CHAPTER 2  MACROFINANCIAL STABILITY AMId hIGh GLOBAL ECONOMIC uNCERTAINTY

Box 2.1 (continued)

Figure 2.1.2. Macroeconomic Uncertainty and Tail Risks to Bank Loan Growth

Higher macroeconomic uncertainty is associated with larger downside risks to
future credit growth ...

... and its impact increases when
financial vulnerabilities are high.

1. Real Economic Uncertainty Index
and Downside Risks to Bank
Lending
(Percentage points, annualized)

2. Bank-Level Measure of

Uncertainty and Downside Risks
to Bank Lending
(Percentage points, annualized)

3. Amplification Effect from

Exposure to Sovereign Risk,
One Year Ahead
(Percentage points, annualized)

0.5

0

−0.5

−1.0

−1.5

−2.0

0.5

0

−0.5

−1.0

−1.5

0

−0.5

−1.0

−1.5

−2.0

−2.5

−3.0

−3.5

Base
With interaction

y
t
n
i
a
t
r
e
c
n
u

c
i

m
o
n
o
c
e
l
a
e
R

t
s
a
c
e
r
o
f
h
t
w
o
r
g

P
D
G

f
o
n
o
i
s
r
e
p
s
i
D

y
t
n
i
a
t
r
e
c
n
u

y
c
i
l
o
p
c
i

m
o
n
o
c
E

d
e
s
a
b
-
t
x
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t

l
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a
B

e
r
u
s
a
e
m
y
t
n
i
a
t
r
e
c
n
u

1 2 3 4 5 6 7 8 9 10 11 12
Quarters

1 2 3 4 5 6 7 8 9 10 11 12
Quarters

Sources: Bank for International Settlements; Fitch Analytics; IMF, Global Data Source and International Financial Statistics databases;
Organisation for Economic Co-operation and Development, Main Economic Indicators database; and IMF staff calculations.

Note: Panels 1 and 2 show the effects of a one-standard-deviation increase in the real economic uncertainty index and the bank-level
text-based uncertainty measure, respectively, on the 10th percentile of the distribution of future aggregate real credit growth at different
time horizons. For each horizon, the growth rate indicates the average quarterly rate (annualized). Results are obtained from a panel
quantile regression using country-level data for both advanced and emerging market economies. Graph lines show estimated effects;
shaded areas represent 90 percent confidence interval. Panel 3 shows the effect of a one-standard-deviation increase in four types of
measures of uncertainty on the 10th percentile of the distribution of one-year-ahead real credit growth interacted with banks’ exposure to
sovereign debt (defined as the share of banks’ holdings of sovereign debt in an economy as a share of banks’ total assets). The “Base” effect
(green bars) shows the coefficient for the measure of uncertainty, and “With interaction” (red bar) shows the effect of uncertainty when banks’
exposure to sovereign debt is one standard deviation above the mean. The measures of uncertainty and sovereign exposure variable are
standardized. Estimated coefficients (solid bars) are statistically significant at the 10 percent level. The error bars represent the 90 percent
confidence intervals for the sum of the coefficients, including both the base effect and the interaction effect. See Online Annex 2.5 for
further details.

increase in the REU is associated with an increase in
the one-year-ahead downside risk to lending (10th per-
centile of the real credit growth distribution) of about

1 percentage point when domestic banks’ exposure to
sovereign risk is high (one standard deviation above
the mean) compared to at the mean level.

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GLOBAL FINANCIAL STABILITY REPORT: STEAdYING ThE COuRSE: uNCERTAINTY, ARTIFICIAL INTELLIGENCE, ANd FINANCIAL STABILITY

Box 2.2. Macroeconomic Uncertainty and Policy Credibility

Many factors, including policy uncertainty, can
drive macroeconomic uncertainty (Bloom 2009).
Several studies document that monetary and fiscal
policy uncertainty can have contractionary effects
(Fernández-Villaverde and others 2015; Husted,
Rogers, and Sun 2020; Beckmann and Czudaj
2021; Mumtaz and Ruch 2023). This box examines
whether rules-based frameworks, or strengthening of
policy frameworks more generally, can reduce policy
uncertainty.

Monetary Policy

Early proponents of monetary policy rules (Henry
Simons, Lloyd Mints, Milton Friedman) argued that
reducing policy uncertainty and its adverse effects on
the real economy (inefficiencies) was the main benefit
of such rules. In their view, inflation expectations
were stabilized through reduced policy uncertainty
(Dellas and Tavlas 2022).1 Empirical evidence shows
that enhanced monetary policy credibility can help sta-
bilize an economy by more firmly anchoring inflation
expectations to target levels (Park 2023; Beckmann
and Czudaj 2024) and that policy rules can play an
important role in reducing uncertainty (Cochrane,
Taylor, and Wieland 2020, and references therein).2
More generally, the degree of soundness of mon-
etary policy frameworks (regardless of whether they
are strictly rules based) may reduce monetary policy

1The modern (that is, since the late 1970s) literature on rules
versus discretion proves theoretically that discretion can generate
inefficiencies even if it does not increase policy uncertainty.
Although this literature places less emphasis on uncertainty,
it does not provide evidence regarding, or argue against, the
connection between policy rules and uncertainty; it establishes
that weaker theoretical conditions are needed to favor rules over
discretion (Dellas and Tavlas 2022).

2Policy uncertainty can also increase when the effectiveness of
policies comes into question (Carney 2016). Available evidence
indicates that high uncertainty can weaken monetary policy
transmission (Castelnuovo and Pellegrino 2018; Lakdawala and
Moreland 2024). A more uncertain response of the economy
to policy stimulus, in turn, exacerbates policy uncertainty,
increasing uncertainty surrounding the extent to which policy
instruments will need to be adjusted to achieve policy goals.

uncertainty and its effect on downside risk to GDP
growth. Figure 2.2.1, panel 1, shows that countries
where inflation expectations deviate more from the
policy (inflation) targets experience higher levels of
economic policy uncertainty. This result suggests that a
weaker policy framework or impaired policy credibility
can amplify economic policy uncertainty. In addi-
tion, in the context of the growth-at-risk framework,
Figure 2.2.1, panel 2, shows that increased macroeco-
nomic uncertainty (real or policy related) has a larger
effect on downside risk to one-quarter-ahead GDP
growth when policy targets were missed by wider
margins over the preceding three years (that is, when
monetary policy frameworks were weaker). These
results support the view that enhanced credibility and
reliance on stronger monetary policy frameworks can
mitigate the adverse implications of increased uncer-
tainty for macrofinancial stability.

Fiscal Policy

Similar arguments in favor of fiscal rules and their
impact on macroeconomic uncertainty apply to fiscal
policy. Several studies have analyzed the effects of fiscal
policy rules on policy variables (such as budget balances
or debt levels), market variables (interest rates and sover-
eign risk premiums), and output cyclicality, concluding
that fiscal rules can reduce fiscal policy uncertainty,
fiscal procyclicality, and market volatility and enhance
fiscal sustainability (Reuter 2015). Fatas and Mihov
(2006), Badinger and Reuter (2017), and Arroyo Mar-
ioli, Fatas, and Vasishtha (2024) document that more
stringent fiscal rules can reduce overall macroeconomic
volatility (and hence real economic uncertainty).3,4

3Discretionary fiscal policy is prone to deficit bias due to
political incentives to delay austerity, leading to excessive deficits
and debt (Alesina and Drazen 1991). Fiscal rules can help offset
this bias by acting as a commitment device to limiting the
government’s incentives to exert discretion (Alesina and Tabellini
1990).

4There is also evidence that increased uncertainty can impair

the effectiveness of fiscal policy (Jerow and Wolff 2022; Liu
2023), suggesting that policy responses themselves could
become more uncertain, potentially magnifying macroeconomic
uncertainty.

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CHAPTER 2  MACROFINANCIAL STABILITY AMId hIGh GLOBAL ECONOMIC uNCERTAINTY

Box 2.2 (continued)

Figure 2.2.1. Soundness of Monetary Policy Framework and Eﬀect of Uncertainty on Future GDP

Credible policy frameworks tend to help reduce economic policy uncertainty and its impact on downside risks to output.

1. Average Economic Policy Uncertainty by Soundness

2. Eﬀect of Uncertainty on GaR Conditional on Soundness

of Monetary Policy Framework
(Index, normalized by base period)

of Monetary Policy Framework
(Percentage points, annualized)

210

170

130

90

50

Low
deviation of
inflation from
target

High
deviation of
inflation from
target

Low
deviation of
inflation from
target

High
deviation of
inflation from
target

2000–09

2010–23

Baseline
Uncertainty with high deviation of inflation from target

Real economic
uncertainty

Economic policy
uncertainty

0

−0.5

−1.0

−1.5

−2.0

−2.5

−3.0

−3.5

−4.0

Sources: Online Annex 2.1; and IMF staﬀ calculations.

Note: In panel 1, high deviation is based on the sample median of the deviation of inflation expectations from the policy inflation target
(absolute value summed over preceding three years). The “high deviation” regime acts as a proxy for the eﬀectiveness of monetary policy
framework across countries, indicating less eﬀective frameworks. Panel 2 shows the eﬀect of a one-standard-deviation increase in measures
of real and economic policy uncertainty on the 10th percentile of the distribution of one-quarter-ahead real GDP growth without (baseline,
dark green bar) and with (interaction term, light green bar) sound monetary policy frameworks. Whiskers (solid bars) indicate 90 percent
confidence intervals (significance). GaR = growth-at-risk.

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GLOBAL FINANCIAL STABILITY REPORT: STEAdYING ThE COuRSE: uNCERTAINTY, ARTIFICIAL INTELLIGENCE, ANd FINANCIAL STABILITY

References

Adrian, Tobias. 2020. “‘Low for Long’ and Risk-Taking.” IMF

Departmental Paper 2020/015, International Monetary Fund,
Washington, DC.

Adrian, Tobias, Nina Boyarchenko, and Domenico Giannone.
2019. “Vulnerable Growth.” American Economic Review
109 (4): 1263–89.

Adrian, Tobias, Dong He, Nellie Liang, and Fabio M. Natalu-
cci. 2019. “A Monitoring Framework for Global Financial
Stability.” IMF Staff Discussion Note 2019/06, International
Monetary Fund, Washington, DC.

Adrian, Tobias, and Nellie Liang. 2018. “Monetary Policy,

Financial Conditions, and Financial Stability.” International
Journal of Central Banking 14 (1): 73–131.

Ahir, Hites, Nicholas Bloom, and Davide Furceri. 2022. “The
World Uncertainty Index.” NBER Working Paper 29763,
National Bureau of Economic Research, Cambridge, MA.

Aït-Sahalia, Yacine, Felix Matthys, Emilio Osambela, and

Ronnie Sircar. 2024. “When Uncertainty and Volatility Are
Disconnected: Implications for Asset Pricing and Portfo-
lio Performance.” Journal of Econometrics, ahead of print,
February 9, 2024.

Alesina, Alberto, and Allan Drazen. 1991. “Why Are Stabiliza-

tions Delayed?” American Economic Review 81 (5): 1170–88.
Alesina, Alberto, and Guido Tabellini. 1990. “A Positive Theory
of Fiscal Deficits and Government Debt.” Review of Economic
Studies 57 (3): 403–14.

Alfaro, Iván, Nicholas Bloom, and Xiaoji Lin. 2022. “The Finance

Uncertainty Multiplier.” NBER Working Paper 24571,
National Bureau of Economic Research, Cambridge, MA.

Arellano, Cristina, Yan Bai, and Patrick J. Kehoe. 2010. “Financial
Markets and Fluctuations in Uncertainty.” Federal Reserve
Bank of Minneapolis Working Paper, Minneapolis, MN.

Beckmann, Joscha, and Robert L. Czudaj. 2024. “Uncertainty
Shocks and Inflation: The Role of Credibility and Expec-
tation Anchoring.” Munich Personal RePEc Archive Paper
119971, University Library of Munich, Munich.

Benford, James, Jonathan D. Ostry, and Robert Shiller. 2018.
Sovereign GDP-Linked Bonds: Rationale and Design. Paris:
Center for Economic Policy Research Press.

Bernanke, Ben. 1983. “Irreversibility, Uncertainty, and Cyclical
Investment.” Quarterly Journal of Economics 98 (1): 85–106.

Berger, Allen N., Omrane Guedhami, Hugh H. Kim, and

Xinming Li. 2022. “Economic Policy Uncertainty and Bank
Liquidity Hoarding.” Journal of Financial Intermediation 49:
100893.

Bhattarai, Saroj, Arpita Chatterjee, and Woong Yong Park. 2020.
“Global Spillover Effects of US Uncertainty.” Journal of Mone-
tary Economics 114: 71–89.

Bialkowski, Jedrzej, Huong Dang, and Xiaopeng Wei. 2022.

“High Policy Uncertainty and Low Implied Market Volatility:
An Academic Puzzle.” Journal of Financial Econometrics 143:
1185–208.

Biljanovska, Nina, Francesco Grigoli, and Martina Hengge.
2021. “Fear Thy Neighbor: Spillovers from Economic
Policy Uncertainty.” Review of International Economics
29 (2): 409–38.

Birru, Justin, and Trevor Young. 2022. “Sentiment and Uncer-

tainty.” Journal of Financial Economics 146: 1148–69.

Blinder, Alan S., Michael Ehrmann, Marcel Fratzscher, Jakob
De Haan, and David-Jan Jansen. 2008. “Central Bank
Communication and Monetary Policy: A Survey of Theory
and Evidence.” Journal of Economic Literature 46 (4):
910–45.

Bloom, Nicholas. 2009. “The Impact of Uncertainty Shocks.”

Econometrica 77 (3): 623–85.

Arroyo Marioli, Francisco, Antonio Fatas, and Garima Vasishtha.

Bloom, Nicholas. 2014. “Fluctuations in Uncertainty.” Journal of

2024. “Fiscal Policy Volatility and Growth in Emerging
Markets and Developing Economies.” International Review of
Economics and Finance 92: 758–77.

Asgharian, Hossein, Charlotte Christiansen, and Ai Jun Hou.
2015. “Effects of Macroeconomic Uncertainty on the Stock
and Bond Markets.” Finance Research Letters 13: 10–16.
Badinger, Harald, and Wolf Heinrich Reuter. 2017. “The Case

for Fiscal Rules.” Economic Modelling 60: 334–43.

Baker, Malcolm, and Jeffrey Wurgler. 2006. “Investor Sentiment
and the Cross-Section of Stock Returns.” Journal of Finance
61 (4): 1645–80.

Baker, Scott R., Nicholas Bloom, and Steven J. Davis. 2016.

“Measuring Economic Policy Uncertainty.” Quarterly Journal
of Economics 131 (4): 1593–636.

Bali, Turan, Stephen Brown, and Yi Tang. 2017. “Is Economic
Uncertainty Priced in the Cross-Section of Stock Returns?”
Journal of Financial Economics 126 (3): 471–89.

Beckmann, Joscha, and Robert L. Czudaj. 2021. “Fiscal Policy
Uncertainty and Its Effects on the Real Economy: German
Evidence.” Oxford Economic Papers 73 (4): 1516–35.

74

International Monetary Fund | October 2024

Economic Perspectives 28 (2): 153–76.

Bloom, Nicholas, Max Floetotto, Nir Jaimovich, Itay Sapor-
ta-Eksten, and Stephen J. Terry. 2018. “Really Uncertain
Business Cycles.” Econometrica 86 (3): 1031–65.

Buch, Claudia M., Manuel Buchholz, and Lena Tonzer. 2015.

“Uncertainty, Bank Lending, and Bank-Level Heterogeneity.”
IMF Economic Review 63 (4): 919–54.

Buch, Claudia M., and Linda Goldberg. 2024. “International
Banking and Nonbank Financial Intermediation: Global
Liquidity, Regulation, and Implications.” Staff Report 1091,
Federal Reserve Bank of New York, New York.

Caldara, Dario, and Matteo Iacoviello. 2022. “Measuring
Geopolitical Risk.” American Economic Review 112 (4):
1194–225.

Carney, Mark. 2016. “Uncertainty, the Economy and Policy.”
Speech delivered at the Bank of England, London, June 30.

Castelnuovo, Efrem, and Giovanni Pellegrino. 2018. “Uncer-
tainty-Dependent Effects of Monetary Policy Shocks: A
New-Keynesian Interpretation.” Journal of Economic Dynamics
and Control 93: 277–96.

CHAPTER 2  MACROFINANCIAL STABILITY AMId hIGh GLOBAL ECONOMIC uNCERTAINTY

Coulombe, Philippe Goulet, Maxime Leroux, Dalibor Steva-
novic, and Stéphane Surprenant. 2022. “How Is Machine
Learning Useful for Macroeconomic Forecasting?” Journal of
Applied Econometrics 37 (5): 920–64.

Christiano, Lawrence J., Roberto Motto, and Massimo Rostagno.
2014. “Risk Shocks.” American Economic Review 104: 27–65.
Cochrane, John H., John B. Taylor, and Volker Wieland. 2020.
“Evaluating Rules in the Fed’s Report and Measuring Dis-
cretion.” In Strategies for Monetary Policy, edited by John H.
Cochrane and John B. Taylor, 217–58. Stanford, CA: Hoover
Institution.

Dellas, Harris, and George S. Tavlas. 2022. “On the Evolution
of the Rules versus Discretion Debate in Monetary Policy.”
Journal of Economic Perspectives 36 (3): 245–60.

Knight, Frank H. 1921. Risk, Uncertainty and Profit. New York:

Houghton Mifflin.

Kozeniauskas, Nicholas, Anna Orlik, and Laura Veldkamp.

2018. “What Are Uncertainty Shocks?” Journal of Monetary
Economics 100 (December): 1–15.

Lakdawala, Aeimit, and Timothy Moreland. 2024. “Firm-Level
Uncertainty and the Transmission of Monetary Policy.”
Review of Economics and Statistics, ahead of print, March 18,
2024.

Lenza, Michele, Inès Moutachaker, and Joan Paredes. 2023.

“Density Forecasts of Inflation: A Quantile Regression Forest
Approach.” ECB Working Paper 2830, European Central
Bank, Frankfurt.

Liu, Yang. 2023. “Government Debt and Risk Premia.” Journal

Dew-Becker, Ian, and Stefano Giglio. 2023. “Cross-Sectional

of Monetary Economics 136: 18–34.

Uncertainty and the Business Cycle: Evidence from 40 Years
of Options Data.” American Economic Journal: Macroeconomics
15 (2): 65–96.

Londono, Juan M., Sai Ma, and Beth Anne Wilson. 2024. “The
Global Transmission of Real Economic Uncertainty.” Journal
of Money, Credit and Banking (May).

Fatas, Antonio, and Ilian Mihov. 2006. “The Macroeconomic
Effects of Fiscal Rules in the US States.” Journal of Public
Economics 90 (1–2): 101–17.

Fernández-Villaverde, Jesús, Pablo Guerrón-Quintana, Keith
Kuester, and Juan Rubio-Ramírez. 2015. “Fiscal Volatility
Shocks and Economic Activity.” American Economic Review
105 (11): 3352–84.

Gilchrist, Simon, Jae W. Sim, and Egon Zakrajšek. 2014.

“Uncertainty, Financial Frictions, and Investment Dynamics.”
NBER Working Paper 20038, National Bureau of Economic
Research, Cambridge, MA.

Gilchrist, Simon, Bin Wei, Vivian Z. Yue, and Egon Zakrajšek.
2022. “Sovereign Risk and Financial Risk.” Journal of Interna-
tional Economics 136.

Goodell, John W., Richard J. McGee, and Frank McGroarty.
2020. “Election Uncertainty, Economic Policy Uncertainty
and Financial Market Uncertainty: A Prediction Market Anal-
ysis.” Journal of Banking & Finance 110: 105684.

Goyal, Amit, and Ivo Welch. 2008. “A Comprehensive Look at
The Empirical Performance of Equity Premium Prediction.”
Review of Financial Studies 21 (4): 1455–508.

Greenwood, Robin, and Samuel G. Hanson. 2013. “Issuer Qual-
ity and Corporate Bond Returns.” Review of Financial Studies
26: 1483–525.

Gu, Shihao, Bryan Kelly, and Dacheng Xiu. 2020. “Empirical
Asset Pricing via Machine Learning.” Review of Financial
Studies 33: 2223–73.

He, Zhiguo, and Arvind Krishnamurthy. 2013. “Intermediary
Asset Pricing.” American Economic Review 103 (2): 732–70.
Husted, Lucas, John Rogers, and Bo Sun. 2020. “Monetary Pol-
icy Uncertainty.” Journal of Monetary Economics 115: 20–36.
Jerow, Sam, and Jonathan Wolff. 2022. “Fiscal Policy and Uncer-

tainty.” Journal of Economic Dynamics and Control 145: 104559.

Jurado, Kyle, Sydney C. Ludvigson, and Serena Ng. 2015.

“Measuring Uncertainty.” American Economic Review 105 (3):
1177–216.

Ludvigson, Sydney C., Sai Ma, and Serena Ng. 2021. “Uncer-
tainty and Business Cycles: Exogenous Impulse or Endoge-
nous Response?” American Economic Journal: Macroeconomics
13 (4): 369–410.

Ma, Yiming, Kairong Xiao, and Yao Zeng. 2022. “Mutual Fund
Liquidity Transformation and Reverse Flight to Liquidity.”
Review of Financial Studies 35 (10): 4674–711.

Mumtaz, Haroon, and Franz Ulrich Ruch. 2023. “Policy Uncer-
tainty and Aggregate Fluctuations: Evidence from Emerging
and Developed Economies.” Policy Research Working Paper
10564, World Bank, Washington, DC.

Obstfeld, Maurice, Jonathan Ostry, and Mahvash Qureshi.

2019. “A Tie That Binds: Revisiting the Trilemma in Emerg-
ing Market Economies.” Review of Economics and Statistics
101 (2): 279–93.

Park, Kwangyong. 2023. “Central Bank Credibility and Mone-
tary Policy.” International Journal of Central Banking 19 (2):
145–97.

Pástor, Ĺuboš, and Pietro Veronesi. 2013. “Political Uncertainty

and Risk Premia.” Journal of Financial Economics 110: 520–45.
Reuter, Wolf Heinrich. 2015. “National Numerical Fiscal Rules:
Not Complied With, but Still Effective?” European Journal of
Political Economy 39 (September): 67–81.

Rey, Hélène. 2013. “Dilemma not Trilemma: The Global Finan-
cial Cycle and Monetary Policy Independence.” Centre for
Economic Policy Research. https://cepr.org/voxeu/columns/
dilemma-not-trilemma-global-financial-cycle-and-mone-
tary-policy-independence

Schmeling, Maik. 2009. “Investor Sentiment and Stock Returns:
Some International Evidence.” Journal of Empirical Finance
16 (3): 394–408.

Segal, Gill, Ivan Shaliastovich, and Amir Yaron. 2015. “Good

and Bad Uncertainty: Macroeconomic and Financial Market
Implications.” Journal of Financial Economics 117: 369–97.
Shiller, Robert. 2003. The New Financial Order: Risk in the 21st

Century. Princeton, NJ: Princeton University Press.

International Monetary Fund | October 2024

75

GLOBAL FINANCIAL STABILITY REPORT: STEAdYING ThE COuRSE: uNCERTAINTY, ARTIFICIAL INTELLIGENCE, ANd FINANCIAL STABILITY

Shiller, Robert. 2013. “Speculative Asset Prices.” Nobel Prize

Valencia, Fabián. 2017. “Aggregate Uncertainty and the Supply

Lecture, Stockholm, December 8.

Soto, Paul. 2021. “Breaking the Word Bank: Measurement

and Effects of Bank Level Uncertainty.” Journal of Financial
Services Research 59: 1–45.

Todorov, Karamfil, and Grigory Vilkov. 2024. “What Could Explain

the Recent Drop in VIX?” BIS Quarterly Review (March).

of Credit.” Journal of Banking & Finance 81: 150–65.
Valkanov, Rossen, and Huacheng Zhang. 2018. “Uncertainty

and the Risk-Return Tradeoff.” Unpublished.

Wu, Wei-Shao, and Sandy Suardi. 2021. “Economic Uncertainty
and Bank Lending.” Journal of Money, Credit and Banking 53
(8): 2037–69.

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ADVANCES IN ARTIFICIAL INTELLIGENCE: IMPLICATIONS FOR
CAPITAL MARKET ACTIVITIES

Chapter 3 at a Glance
 • Generative artificial intelligence and related breakthroughs have the potential to dramatically increase the
efficiency of capital markets—trading, investment, and asset allocation—through artificial intelligence–
assisted process automation and analysis of complex unstructured data, and evidence suggests these effects
are already beginning to be felt.

 • New evidence from labor markets and patent filings suggests that the adoption of artificial intelligence (AI) in
capital markets is likely to increase significantly in the near future, and analyses of pricing patterns and trading
dynamics already show changes in some markets consistent with the adoption of these new technologies.
 • In addition, AI could cause large changes in market structure through the greater and more powerful use
of algorithmic trading and novel trading and investment strategies, which in turn may increase turnover
and asset correlations and drive prices to reflect new information at an ever-increasing speed.

 • However, based on outreach conducted with both market participants and regulators, most current use of
AI appears to be an extension of existing trends in the use of machine learning and other advanced analyt-
ical tools; more significant changes are a medium- to long-term concern.

 • AI may actually reduce financial stability risks by enabling superior risk management, deepening market
liquidity, and improving market monitoring by both participants and regulators. At the same time, new
risks may arise:
 ◦ Increased market speed and volatility under stress, especially if trading strategies of AI models all

respond to a shock in a similar manner or shut down in response to an unforeseen event.

 ◦ More opacity and monitoring challenges, as AI spurs further migration of market-making and invest-
ment activities to hedge funds, proprietary trading firms, and other nonbank financial intermediaries
and creates uncertainty about how AI models used by different investors and traders could interact.
 ◦ Increased operational risks as a result of reliance on a few key third-party AI service providers that dom-

inate computational power and large language model services.

 ◦ Increased cyber and market manipulation risks, particularly in generating fraud and social media

disinformation.

 • Many of these risks are addressed by existing regulatory frameworks, but important new and unforeseen
developments may arise. To ensure relevant authorities are prepared for these potentially transformative
changes, they should consider additional policy responses:
 ◦ Undertake the calibration of circuit breakers and a review of margining practices in light of potentially

rapid AI-driven price moves.

 ◦ Enhance monitoring and data collection of the activity of large traders, including nonbank financial

intermediaries.

 ◦ Address dependency on data, models, and technological infrastructure by requesting a risk mapping

from regulated entities (that is, data on the internal and external interconnections and interdependen-
cies that are necessary to deliver the institutions’ critical services).

 ◦ Adopt a coordinated approach for the definition of critical AI third-party service providers and con-

tinue to strive for resilience in capital markets by enhancing cyberattack protocols.

 ◦ Adopt measures that ensure continued market integrity, efficiency, and resilience of over-the-counter

markets when AI use proliferates.

The authors of this chapter are Nassira Abbas (co-lead), Gabriela Elizabeth Conde Vitureira, Mohamed Diaby, Gonzalo Fernandez Dionis,
Andrew Ferrante, Dirk Jan Grolleman (co-lead), Johannes Kramer, Xiang-Li Lim, Benjamin Mosk (co-lead), Puja Singh, and Richard Stobo,
under the oversight of Charles Cohen and Jason Wu. Markus Pelger served as an external advisor.

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CHAPTER3GLOBAL FINANCIAL STABILITY REPORT: STEAdYING ThE COuRSE: uNCERTAINTY, ARTIFICIAL INTELLIGENCE, ANd FINANCIAL STABILITY

Introduction

Artificial intelligence (AI) has the potential to
reshape the world and transform industries, includ-
ing financial services. This chapter focuses on the
use of AI and GenAI1 in capital markets, which
may see deep changes in market structure changes
from network effects and increased speed of market
functioning. Financial services are well poised to
take advantage of recent advances in AI given the
industry’s long-standing focus on data collection and
analysis and early adoption of techniques such as
machine learning (ML). Recent surveys of financial
institutions reported that a vast majority of respon-
dents expect a significant expansion of the use of
GenAI-driven models (IIF and Ernst & Young 2023),
and more than half of investment managers said that
they planned to use GenAI in the future (Mercer
Investments 2024). Hence, it is important to under-
stand the potential financial stability implications of
these developments and to ensure regulators are ready
for these changes.

Further adoption of AI may contribute positively
to financial stability, and can provide clear benefits
to financial institutions, such as efficiency improve-
ments and higher productivity (Boukherouaa and
others 2021), refined portfolio investing frameworks
(Park and others 2023), improved return forecasting
(Chen, Kelly, and Xiu 2023), and quantification of
crash risks (Swinkels and Hoogteijling 2022). There
are also AI applications benefiting SupTech and
RegTech.2

However, AI could also introduce new forms of
financial stability risks and accelerate well-established
financial stability concerns such as leverage, liquidity
strains, and interconnectedness. This chapter considers
and finds indicative evidence for four broad catego-
ries of potential risks, which could transmit stress to
the real economy through loss of market confidence,

1For the purpose of this chapter, AI or “machine learning (ML)

models” (AI/ML) refers to well-established predictive analytics,
including shallow neural networks, clustering algorithms, textual
analysis tools natural language processing, decision trees, and so
on; and “sophisticated AI models” refers to their move recent and
advanced counterparts, such as deep neural network architectures
addressing reinforcement learning, and natural language process-
ing (large language models). This includes GenAI models capable
of generating text, codes, images, and other content.

2SupTech and RegTech are advanced financial technology applica-

tions used by supervisors and regulated institutions.

higher borrowing costs, and potentially significant
financial system outages:
 • Increased market speed and volatility under

stress, especially if AI trading strategies become
highly correlated

 • Opacity and monitoring challenges as extreme
behaviour of AI systems becomes increasingly dif-
ficult to anticipate and AI activities also migrate to
nonbank financial intermediaries (NBFIs)

 • Increased operational risks as a result of reli-

ance on a few key third-party AI service providers

 • Increased cyber and market manipulation risks,
particularly through fraud and disinformation

GenAI is already seeing widespread “evolutionary”

adoption—use cases that build upon existing ana-
lytical methods and investment strategies—across
the financial sector. As in other industries, GenAI
is increasing efficiency across a host of tasks: help-
ing analysts write code, improving customer-facing
activities, and generating new investment ideas. Large
language models are being used as inputs into existing
analytical models to improve the forecasting power
of textual analysis, likely improving the predictive
power of quantitative investment strategies. It could
also lower barriers to entry for quantitative inves-
tors into less liquid asset classes (such as corporate
or sovereign bonds) that require extensive analysis
of indentures and other legal documents. GenAI is
also likely to increase the speed of market reactions
to new information through the real-time processing
of unstructured data, such as textual central bank
announcements. Numerous other use cases in asset
allocation, trading, and risk management have been
noted by market participants (Figure 3.1).

The more “revolutionary” uses of GenAI—radically

new investment strategies and processes using
cutting-edge AI technology—remain mostly specu-
lative. Although many observers envision scenarios
involving autonomous AI generating and executing
trades without human oversight, most market par-
ticipants that responded to IMF outreach are quite
uncomfortable with this idea (Box 3.1). They view
AI-generated strategies that are not understood by
humans as a nonstarter. In addition, for regulatory,
risk management, liability, and ethical reasons, most
participants view having a “human in the loop” as an
essential part of any AI-based strategy.

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International Monetary Fund | October 2024

CHAPTER 3  AdvANCES IN ARTIFICIAL INTELLIGENCE: IMPLICATIONS FOR CAPITAL MARkET ACTIvITIES

Figure 3.1. Recent and Potential Use Cases for Artificial Intelligence and Machine Learning in Capital Market Activities:
Investment Decisions, Trade Execution, and Monitoring Processes

Potential benefits include enhanced accuracy, eﬃciency, and market insights through multidimensional analysis from unstructured data sources,
delivering customized, and actionable outputs.

Key
Processes

Potential
Benefits
from
Adopting AI

Client/Institution
Profiling

Identification of
Needs and
Constraints

Enhance client’s
profile assessment
• Analyze

unstructured or
alternative clients’
data to understand
unique objectives,
idiosyncratic
needs, and risk
preference
• Generate
simulated
scenarios and
visualization of
potential
outcomes of
diﬀerent asset mix

Asset Allocation

Trading

Risk Management

Asset Class
Allocation

Sectoral
Allocation

Security
Selection

Orders Placement and
Execution

Risk Monitoring

Reporting

Improve analysis
precision
• Feature

Minimize market
impact
• Structured trade

Enhance optimization
and forecast techniques
for strategic allocation
• High dimensional
forecasting and
predictor selections

• Deep learning

methodologies for
dynamic multiperiod
portfolio optimization

• Clustering/network
analysis to analyze
multidimensional
interactions/
correlations

extraction
(beta,
momentum,
and so on)
• Network/

multidimension
analysis for
relative value
analysis and
identify price
dislocation

Derive signals from unstructured and
alternative data
• Natural language processing models for
sentiment analysis to identify thematic
opportunities

• Polarity detection, microtext analysis, aspect
extraction, or sarcasm detection to improve
signal quality

execution algorithms
to minimize market
impact

• Analyzing unstructured
data and cross-market
indicators to identify
prevailing liquidity
conditions

Assist price discovery
• Modelling executable
prices for illiquid secu-
rities through multiple
market indicators
Improve liquidity
management eﬃciency
• Forecast liquidity
needs (margin
management,
collateral, etc.) through
clustering/network
analysis

Customize
insights
• Customized
content
generation,
reports, and
dashboards

• Chatbot
Ease
compliance
monitoring
• Screening,

flagging, and
reporting of
anomalies

Dynamic risk
sensing
• Generate risk
hypothesis
• To identify

performance
drivers and
anomalies
through
multidimensional
analysis
Generate risk
scenario
• Value-at-risk
estimation
through
generative
adversarial
networks to
capture temporal
dynamics in
time-series data

Sources: Academic studies; IMF outreach discussions (see Box 3.1); prospectus from third-party services; and IMF staﬀ compilations.

Note: The figure presents recent and potential artificial intelligence (AI) and machine learning (ML) use cases across investment decision, execution, and monitoring
processes. The information may not be exhaustive of all possible AI/ML use cases, as adoption continues to evolve.

For emerging markets, AI is widely seen as a positive
development, although it may create fragmentation risks.
The IMF’s outreach effort found that market participants
widely viewed GenAI as a tool to enable technological
leapfrogging and increase financial development and
inclusion for many emerging market and developing
economies through increased access to credit and a deep-
ening of local financial markets. However, if high fixed
costs lead to different speeds of adoption across regions,
emerging market and developing economies may be less
able to benefit from the migration to AI-driven activities
than advanced economies.

As adoption is still at a relatively early stage, this

chapter gives a forward-looking assessment of the

impact of AI (and GenAI specifically) on capital
markets. To compensate for the lack of readily available
data in this area, the chapter draws on a combination
of extensive IMF staff market outreach (Box 3.1) and
analytical work that leverages novel data sources. By
understanding the current levels and speed of adoption
of AI, the chapter posits where and how AI-related
risks may arise. It analyzes how AI is transforming
market structures and dynamics and examines the
financial stability implications for liquidity, leverage,
and interconnectedness as well as other potential
novel risks. The chapter concludes by offering policy
recommendations that focus on monitoring and the
sufficiency of current or forthcoming guidelines.

International Monetary Fund | October 2024

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GLOBAL FINANCIAL STABILITY REPORT: STEAdYING ThE COuRSE: uNCERTAINTY, ARTIFICIAL INTELLIGENCE, ANd FINANCIAL STABILITY

Figure 3.2. The Cost of Compute and the Artificial Intelligence Market Structure

Cost per unit of calculation has declined but models are increasingly
complicated ...

... but most large, foundation models are being developed by a
concentrated number of countries.

1. Cost of a Single Precision Processor and the Cost of Training Notable

2. National Aﬃliation of Foundation Models and Access Type

AI Models
(Inflation-adjusted 2023 US dollars per exa floating point operation; millions
of inflation-adjusted 2023 US dollars, right scale)

(Trailing six-month count of published models; trailing six-month percentage
of open-source models, right scale)

Cost of a single precision processor
Cost of training notable AI models (right scale)

50

40

30

20

10

North America
China
Europe
Asia excl. China
Others
Percent of open-source models (right scale)

50

150

40

120

30

90

20

60

10

30

0
2011

13

15

17

19

21

0

23

0

Feb. 2021

Nov. 2021

Aug. 2022

May 2023

Feb. 2024

100

80

60

40

20

0

Sources: Epoch AI; Stanford University’s Ecosystem Graphs; and IMF staﬀ calculations.

Note: Training compute is measured using floating-point operations. Estimated costs for panel 1 were derived from Epoch AI’s data sets and the methodology to estimate
costs of training notable artificial intelligence models and graphics processing unit price-performance data. Foundation models are artificial intelligence/machine learning
models developed that can be used for various applications. Exa denotes a factor of 1018. The blue and red lines in panel 1 are best-fit lines. AI = artificial intelligence.

Current and Future Adoption of Artificial
Intelligence in Capital Market Activities

Mainstream use of GenAI only dates back a few
years, but financial institutions have been actively using
ML and other AI-related computation methods for
approximately 20 years, and these methods are now well
integrated into their investment processes. Robo-advis-
ing, AI-based exchange-traded funds (ETFs), and appli-
cations related to GenAI are only in their infancy, but
labor market data, patent filings, and investor outreach
all suggest that institutions are rapidly gearing up for
significant integration of these technologies.

Technological Change and the Rise of
Artificial Intelligence

Although the unit cost of training AI models has
dropped dramatically as a result of recent advancements
in algorithmic efficiency and computation hardware,
“notable” models of the type used in leading GenAI
applications have simultaneously become much more
complex, leading to much higher overall costs (Figure 3.2,
panel 1).3 The high fixed costs of the infrastructures and

3Notable models are models in the running for the top 10 largest

training compute, expressed in terms of required floating-point
operations (FLOP) (Epoch AI 2024).

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International Monetary Fund | October 2024

talent enabling development and distribution of sophis-
ticated AI systems may exacerbate market concentration,
whereby the few private sector developers with existing
commercialization channels could continue to dominate
the space (noting that the growing number of open
source models may challenge this paradigm). Concentra-
tion often arise also because of data monopolies, whereby
some players have access to superior nonpublic data,
which would allow them to train more effective models
or have the capacity to process huge volumes of data. This
is especially pertinent in the financial sector, where some
players have vast amounts of trading and client data.
Development of foundation models has predominantly
been based in the United States (Figure 3.2, panel 2).

Current Adoption: Evidence from the
IMF’s Market Outreach

From a capital market perspective, the expansion and

considerable scale of robo-advising highlights a move
toward automation by the investment industry (Figure
3.3, panel 1). However, genuinely AI-driven strategies
are still in their early stages. For instance, AI-powered
ETFs—where AI is used to construct and adjust an
ETF’s portfolio—still account for a very small share
of the market, with less than $1 billion in assets under
management (Figure 3.3, panel 2). This indicates that

CHAPTER 3  AdvANCES IN ARTIFICIAL INTELLIGENCE: IMPLICATIONS FOR CAPITAL MARkET ACTIvITIES

Figure 3.3. Investment Strategies Driven by Artificial Intelligence

Robo-advisor assets under management have grown explosively and are
projected to grow further.

AI-driven exchange-traded fund (ETF) investment has grown, but it
remains tiny compared to the market’s size.

1. Robo-Advisors’ Assets Under Management

2. AI-Driven ETF Assets Under Management

(Billions of US dollars; share of US equity market cap in percent)

(Billions of US dollars; share of US equity market cap in percent)

Assets under management (AUM)
AUM as a share of US equity market cap in percent (right scale)

2017

18

19

20

21

22

23 24:Q3 25F

26F

27F

5

4

3

2

1

0

1.0

0.8

0.6

0.4

0.2

0

Fund assets under management (AUM)
AUM as a share of US equity market cap in percent (right scale)

2018

19

20

21

22

23

24

0.003

0.002

0.002

0.001

0.001

0

2,500

2,000

1,500

1,000

500

0

Sources: Bloomberg Finance L.P.; Statista Digital Market Insights; and IMF staﬀ calculations.

Note: In panel 1, the light blue bars are the forecasts for 2025, 2026, and 2027. The share of assets under management as a percentage of the US equity market
capitalization is based on the MSCI US Equity Index market capitalization. AI = artificial intelligence; ETF = exchange-traded fund.

although technology has begun to alter the landscape of
investment management, the penetration of advanced
AI applications is relatively modest.

To complement the analytical work based on an
extensive literature of review and data collection, IMF
staff conducted a qualitative assessment with main
players in the industry directly involved in AI-related
strategy to further assess how AI advances have been
adopted and are transforming capital markets. The
IMF staff outreach aimed to shed light on how finan-
cial institutions—both buy-side and sell-side firms—
are harnessing AI technologies. While acknowledging
that AI is not a new phenomenon, all market partici-
pants highlighted the accelerating pace of AI adoption
in various areas, mainly driven by the proliferation of
GenAI tools (Box 3.1).

Prospects for creating value through AI appear to be

most promising in publicly traded liquid asset classes
(Figure 3.4, panel 1).4 Equities, government bonds, and
listed derivatives offer a wealth of real-time data and
transparency. The high volume of transactions and the
dynamic nature of these markets enable AI systems to
continuously learn and adapt, potentially offering more
accurate and timely insights. Results from the IMF’s

4For a thorough description of capital market structure (for
example, type of instruments, actors, trading venues, and central
counterparties), see US Securities and Exchange Commission (2020).

outreach to stakeholders point to equities and derivatives
as being the most likely areas where AI will be adopted
in the investment process, followed by fixed income and
foreign exchange (which are primarily traded in over-
the-counter markets) (Figure 3.4, panel 2). However,
some market participants also highlighted that advances
in AI and its unprecedented processing capabilities
could benefit less-liquid markets such as private credit
and some emerging markets segments.

The IMF’s outreach also reveals a number of AI
use cases in the investment process. For instance, AI is
used in the incorporation of alternative data sets,5 the
development of forward-looking indicators, and market
analysis.6 More specifically, buy-side firms employ
AI/ML for productivity enhancement, including
exploration of new asset classes,7 extraction of signals
from data to support their investment decisions, and

5Alternative data sets include content from social media platforms
and other public forums where market participants share their opin-
ions and engage in discourse. Sentiment analyses, although various
natural language processing methodologies, are also conducted on
regulatory filings or relevant public statements.

6Some market participants also employ AI techniques on price
movements from other asset classes or instruments to estimate valua-
tion and executable prices for some illiquid instruments.

7AI is primarily adopted for asset-class research, focusing on

summarizing research documents from various sources and extracting
key information relevant to assessing risk and return profiles or
requirements that are unique to individual investors.

International Monetary Fund | October 2024

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GLOBAL FINANCIAL STABILITY REPORT: STEAdYING ThE COuRSE: uNCERTAINTY, ARTIFICIAL INTELLIGENCE, ANd FINANCIAL STABILITY

Figure 3.4. Opportunities for Artificial Intelligence to Create Value: Asset Classes

There is a strong correlation between market liquidity and current
adoption of AI by investment managers.

The IMF’s outreach to stakeholders suggests equity markets as most likely
to see AI implementation.

s
e
i
t
i

n
u
t
r
o
p
p
o
n
o
i
t
a
e
r
c
e
u

l
a
v
I
A

1. AI Opportunities and Asset Class Liquidity

(Value creation opportunity index; liquidity score)

1.0

0.9

0.8

Publicly traded assets
Privately traded assets

Commodities

Equities

Private equities

Fixed income

Real estate

0.7

Private debt

Natural capital

Infrastructure

0.6

0

2

6
4
Liquidity score

8

10

2. Which Markets Are Incorporating Machine Learning and

Generative AI?
(Percent of participants’ scores)

18%

11%

14%

Equities and derivatives
Fixed income
Foreign exchange
Commodities

57%

Sources: IMF, October 2024 Global Financial Stability Report market intelligence; Mercer Investments (2024); and IMF staﬀ analysis.
Note: In panel 1, the liquidity score is assigned by asking a large language model to assign a liquidity score between 1 and 10 to each asset class, whereby the model is
prompted to assign an ordinal score, distinct for each asset class, reflecting the liquidity of the given asset classes, and 1 reflects the least liquid asset class and 10 the
most liquid. The AI value creation opportunities score is a ratio based on survey responses, whereby the numerator is equal to diﬀerence between the respondents that do
see value creation opportunities and those that do not; the denominator is equal to the number of respondents that expressed a view. Details on the market intelligence
outreach in panel 2 can be found in Box 3.1. AI = artificial intelligence.

portfolio optimization and allocation, as well as for
back-office activities. Meanwhile, sell-side institutions
use AI/ML for risk assessment, pricing and forecasting,
and customer service and to improve trading automa-
tion. Market infrastructure providers and academia note
that AI/ML models, including sophisticated AI models,
are aiding the democratization of techniques such as
code writing and prototyping as well as information
extraction and summarization.

Participants in the IMF’s outreach to stakeholders
widely observed that recent breakthroughs, particularly
in GenAI, are catalyzing broader AI/ML adoption
across capital markets. Within a three- to five-year
horizon, participants expect greater integration of
sophisticated AI in investment and trading decisions.
One use case gaining traction in asset management is
the AI-powered exploration of alternative and text data
to uncover causal relationships in markets that are pre-
viously unknown, which could lead to new investment
strategies. Another would be the adoption of tradi-
tional AI/ML applications to increase the robustness
and accuracy of existing models, especially in terms
of forecasting. A recent survey by Mercer Invest-
ments (2024) shows that the adoption of AI in core
investment processes such as trading and the execution

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International Monetary Fund | October 2024

of investment decisions is still nascent (Figure 3.5,
panel 1). Concentrating on the more specialized area
of algorithmic trading, evidence is mixed. Survey
data among participants of a major energy market
(The Netherlands) suggest that more autonomous
algorithms may still be based on simpler methods
(Figure 3.5, panel 2).8

Meanwhile, there is evidence that sophisticated AI
has not yet been implemented widely to build auton-
omous AI trading agents (Authority for Consumers
and Markets 2024, p. 18). It is instead more frequently
used to generate a signal that is then used as an input
in an existing analytical system where a human trader
may ultimately make the trading decision. There was
a consensus among the IMF outreach participants on
the increasing benefits of AI/ML, including improved
efficiency and productivity, cost savings in designing

8The Dutch Authority for the Financial Markets found that on

the Euronext exchange, “trading firms tell the Authority for the
Financial Markets that machine learning is implicitly or explicitly
used in 80 to 100 percent of their trading algorithms.” It should be
noted however, that “explicit” use cases may include applications
that are not autonomous, such as signal generators (Authority for the
Financial Markets 2023).

CHAPTER 3  AdvANCES IN ARTIFICIAL INTELLIGENCE: IMPLICATIONS FOR CAPITAL MARkET ACTIvITIES

Figure 3.5. Artificial Intelligence Advances: Use Cases and Adoption in Investment Processes

Adoption of AI in trading and investment decision making is still nascent.

1. Use of AI by Investment Managers

(Percent)

Already integrated
Used, but not integrated

Partly integrated
Do not plan to integrate

Adoption of machine learning has not yet penetrated autonomous trading
processes.

2. Share of Dutch Energy Market Algorithms Based on Machine Learning

(Percent)

Planning to integrate

Share of algorithms based on ML

Not based on ML

Incorporating alternative data sets

43

15

7

6

28

Developing forward-looking indicators

37

11

16

9

27

Analyzing market indicators

Generating ideas

33

30

22

7

6

31

23

9

12

26

Executing investment decisions

11 11

15

4

Allocating assets

10

9

12 10

59

59

0

50

100

Signal generator

Trading algorithm

Execution algorithm

100

90

80

70

60

50

40

30

20

10

0

Sources: Authority for Consumers and Markets; IMF, Mercer Investments (2024); and IMF staﬀ analysis.

Note: Panel 2, statistics are derived from survey responses regarding algorithms that are already in use; survey responses regarding planned use cases are not included.
AI = artificial intelligence; ML = machine learning.

trading algorithms, better processing of unstructured
data, and more compressed bid-ask spreads.

Financial supervisors included in the IMF’s staff
outreach indicated that they were beginning to reap
the benefits of AI. They use AI-driven SupTech tools
to monitor financial markets and institutions, includ-
ing ones that detect anomalies in large data sets to
identify risks early, and other tools that can help check
regulatory compliance of supervised entities. For their
part, banks have used RegTech tools to manage regu-
latory compliance and to enhance and boost efficiency
of their “anti-money laundering/know your customer”
process by, for instance, automating some tasks to
ensure higher accuracy in clients’ data, monitor trans-
actions, and detect fraud.

Looking ahead, market participants expect a rise in the
use of AI in trading and investment, and a higher degree
of autonomy of AI-based decisions, especially in the
equity market, where high-frequency, AI-driven trading
is expected to account for a more substantial share. How-
ever, all participants in the IMF’s outreach expected a
“human in the loop” approach to persist in the near term
(three to five years), especially for large capital allocation
decisions. Although the trend is toward less human
interaction, complete autonomy is not anticipated soon,
and models will continue to operate within predefined

rules. Some participants mentioned the potential for
agent-to-agent trading and the development of complete
AI-driven workflows in trading.

Future Adoption: Evidence from Patent Filings
and Labor Markets

The relationship between financial innovation and

patents has been an area of growing interest in the
literature (Lerner and others 2024). Financial innova-
tions have become more significant and economically
impactful, with a notable increase in patent grants today
compared to the 1990s. This trend provides valuable
insights into the evolving nature of financial innovation.
In this regard, the evolution of AI patent filings may
serve as an indicator of AI adoption in capital market
activities, providing insights into future trends.

The number of filings that reference AI/ML termi-
nologies in the context of high-frequency or algorithmic
trading has increased (Figure 3.6, panel 1). Over the
past year, filings lean toward improving operational
efficiency of brokerage or trading platforms and on
developing systems that compute trading signals with
low latency and high throughput. AI/ML-related filings
have also driven a surge in patents in the area of asset
management (Figure 3.6, panel 2). Filings related to

International Monetary Fund | October 2024

83

GLOBAL FINANCIAL STABILITY REPORT: STEAdYING ThE COuRSE: uNCERTAINTY, ARTIFICIAL INTELLIGENCE, ANd FINANCIAL STABILITY

Figure 3.6. Artificial Intelligence/Machine Learning Innovations: Evidence from Patent Applications

Filings relating to high-frequency or algorithmic trading incorporating
AI/ML are increasing ...

... with a similar trend observed for applications relating to broader asset
management practices.

1. Patents Related to High-Frequency/Algorithmic Trading

2. Patents Related to Asset Allocation or Portfolio Management

(Number of patents; percent of patents)

(Number of patents; percent of patents)

80

70

60

50

40

30

20

10

0

Algo/HFT
of which contains AI/ML term
AI/ML-share (right scale)

2009 10 11 12 13 14 15 16 17 18 19 20 21 22 23

70

60

50

40

30

20

10

0

210

180

150

120

90

60

30

0

AA/PM
of which contains AI/ML term
AI/ML-share (right scale)

2009 10 11 12 13 14 15 16 17 18 19 20 21 22 23

80

70

60

50

40

30

20

10

0

Sources: World Intellectual Property Organization, PATENTSCOPE; and IMF staﬀ calculations.

Note: Aggregated values are limited to filings with specific mention of financial or capital markets and the relevant terminologies. Aggregate patents may not be unique
patents, as some patents may be filed in multiple jurisdictions. Aggregate patents may not be exhaustive of all patents filed with respective national authorities and are
limited to those available in the PATENTSCOPE database. These filings may or may not translate to actual patents granted by national authorities, and the review process
may take time depending on the complexity of the invention. In panel 2, asset management practices also include asset allocation and portfolio management
applications. AA = asset allocation; AI = artificial intelligence; HFT = high-frequency trading; ML = machine learning; PM = portfolio management.

asset management detail the use of ML techniques
to enhance the efficiency of cash flow and liquidity
management, automate asset class rebalancing, improve
valuation and forecasting methods, and determine
capital requirements tailored to individual needs. Several
innovations focus on interpreting unstructured data and
designing systems to process information from alterna-
tive data sources. In addition, some filings incorporate
techniques to access and manage alternative asset classes,
such as methodologies for trading emissions and man-
aging digital assets, as well as evaluation methods for
validating cryptographically signed transactions.

Although only a small share of workers claims
to have AI skills (Figure 3.7, panel 1), the talent
pool, specifically within the financial services indus-
try, appears to be growing. Quantitative researchers
and analyst profiles in the US financial industry
increasingly feature AI skills. ML, natural language
processing, and deep learning are among the top
30 competencies listed in their profiles.9 Demand for
AI skills is on the rise and, according to the IMF’s out-

9Ranking is based on LinkedIn’s statistical measure using Term
Frequency-Inverse Document Frequency, a natural language process-
ing algorithm that evaluates how representative a word/terminology
is. Specifically, ML ranks among the top five skills in this cohort,
highlighting the industry’s expanding focus on AI/ML applications.

84

International Monetary Fund | October 2024

reach, competition to attract talent is one of the most
important challenges that could limit the acceleration
of developments in AI. The incorporation of AI skills
in job postings for typical front office roles with direct
influence on investment decision making or responsi-
ble for financial market transactions has been increas-
ing,10 and the share of job postings for these front
office roles and the financial services industry requiring
AI skills has outpaced the overall share of AI-related
job postings for the broader US economy (Figure 3.7,
panel 2). Unsurprisingly, AI talent concentration11
within the US financial services industry also exceeds
the broader economy.

According to participants in the IMF’s outreach, AI
could bring greater financial opportunities in emerging
markets and developing economies. Cited key benefits
include improvements in access to financial services,
credit scoring, loan origination, robo-advising, and

10Also known as “front office” roles, including traders, portfolio
managers, portfolio strategist, asset allocation analysts, and program-
matic traders. Job postings containing AI terminology for these roles
rose from a monthly average of 4.5 percent of front office roles in
2019 to 4.9 percent in 2023 and a peak of 6.6 percent in 2022.

11A LinkedIn member is considered AI talent if they have explicitly
added AI skills to their profile and/or they are occupied in an AI occu-
pation representative, which requires AI skills to perform the job.

CHAPTER 3  AdvANCES IN ARTIFICIAL INTELLIGENCE: IMPLICATIONS FOR CAPITAL MARkET ACTIvITIES

Figure 3.7. Adoption of Artificial Intelligence: Evidence from Candidate Profiles and Job Vacancies

While the existing workforce in the broader industry is steadily adopting
AI skillsets ...

... demand for these skills, particularly in the financial services sector, has
increased in recent years and appears to outpace job postings of the
broader US economy.

1. Concentration of LinkedIn Members in the Financial Services Industry

2. Job Postings Containing AI Skills and the Share of AI Talent

with AI Skills
(Average percent of aggregated country profiles)

AEs

EMs

(Average monthly percent of total job postings, percent of total members)

2019

2023

6

4

2

0

1.5

1.2

0.9

0.6

0.3

US financial
services: front
oﬃce roles

US financial
services

US

US financial
services

US

AI-related job postings
(average monthly percent of total postings)

AI talent concentration
(percent of total members)

0
2016

17

18

19

20

21

22

23

Sources: Indeed Hiring Lab; LinkedIn Economic Graph; and IMF staﬀ calculations.

Note: Financial services include entities that make financial transactions (creation, liquidation, or change in ownership of financial assets) and/or that facilitate financial
transactions across 30 advanced economies and 14 emerging markets. Data from Indeed and LinkedIn were obtained through the Development Data Partnership
(https://datapartnership.org/), a collaboration between international organizations and private sector companies to facilitate the eﬃcient and responsible use of
third-party data in international development. AEs = advanced economies; AI = artificial intelligence; EMs = emerging markets.

portfolio construction. GenAI-enabled parsing of frag-
mented and unstructured data could reduce investment
barriers in these countries and improve the liquidity
of some emerging market assets. Synthetic (AI-gener-
ated) data may also be helpful in training investment
models where data are scarce, bearing in mind the
caveats around the use of this technology. Overall, the
combination of better liquidity and enhanced market
efficiency could make some emerging markets more
attractive to global investors and potentially lead to
larger capital flows.

The use of a new generation of models should help
address data gaps, thanks to the use of synthetic data
in less-efficient markets, in turn enhancing mar-
ket liquidity and lowering barriers to entry. Indeed,
synthetic data being real data-like and generated by
algorithms, can indeed offer valuable opportunities for
training and testing AI. However, reliance on gener-
ated data should account for two key issues. First, the
unintended over- or under-representation of certain
values of real-world data distribution, undermining
extreme event performance of AI systems. Second,
potential biases perpetuated by synthetic data when the
generation process fails to account for specificities and
requirements of second-order applications.

Other market participants indicate possible differ-
entiation between large and less-significant emerging
markets, based on the extent to which AI technolo-
gies will be implemented. The risk of fragmentation
between advanced economies and emerging market
and developing economies seems to be limited, and
some market participants reported that advances in
AI could instead support greater financial inclusion.
AI-driven financial services may facilitate access to
credit using new data sets where traditional met-
rics are less developed, and robo-advising should
reduce barriers to entry in investing, deepening local
capital markets. However, others pointed to the risk
of automation affecting lower-skilled jobs in some
countries.

The Artificial Intelligence
Transformation: Implications for
Market Structures and Dynamics

The adoption of AI in capital market activities has
the potential to change the structure and dynamics of
markets. Some of these changes are more evolution-
ary, whereby existing trends may be amplified. Other
impacts could be more revolutionary: For example, the

International Monetary Fund | October 2024

85

GLOBAL FINANCIAL STABILITY REPORT: STEAdYING ThE COuRSE: uNCERTAINTY, ARTIFICIAL INTELLIGENCE, ANd FINANCIAL STABILITY

Figure 3.8. Algorithmic Trading Activity and Concentration in Equity Markets
(Percent)

Algorithmic trading has expanded across asset
classes.

The US equity market, being the largest and
most liquid market, has seen the most
aggressive adoption of algorithmic trading.

Activity in markets dominated by algorithmic
trading tends to be dominated by a few players.

1. Share of Algorithmic Trading Activity by

2. Equity Market Algorithmic Trading Activity

3. Euronext Equity Market Trading Activity by

Asset Class in the United States

by Region

Equities
Foreign exchange
Fixed income

Futures
Options

United States
Europe
Asia
Latin America

80

60

40

20

0
2004

07

10

13

16

19

22

2023

80

60

40

20

0

Most Active Participants

Individual contribution
Cumulative

1

2

3

5

6

4
7
Rank member

100

90

80

70

60

50

40

30

20

10

0

8

9

10

Sources: Authority for the Financial Markets; Datos Insights; and IMF staﬀ calculations.

Note: In panel 1, “equities” refers to US equities. In panel 3, statistics show trading activity by most active market participants on Euronext.

prospect of a market with competing and self-learning
algorithms opens up a range of possible new market
structure outcomes. This section first explores how AI
could amplify existing trends and then turns to more
revolutionary aspects.

A Larger Role for Nonbank Financial Institutions
and More Algorithmic Trading

With the help of AI models, NBFIs may grow even
more important, and the largest ones more important
still. NBFIs now hold over half of all financial market
assets globally. They are generally more agile and sub-
ject to fewer constraints with regard to the adoption
of AI. By contrast, some of the larger banks may suffer
from legacy infrastructure and may be subject to more
stringent requirements in terms of model governance
and accountability, and model explainability.

Over the past two decades, financial markets in
advanced economies have experienced a significant
transformation with the growth of algorithmic trading,
with NBFIs rising to newfound importance. In the
United States, algorithmic trading now constitutes about
70 percent of equities trading and more than half of
futures trading (Figure 3.8, panel 1). Other jurisdictions

are lagging behind in the share of algorithmic equities
trading, but they could catch up briskly (Figure 3.8,
panel 2). Increasing returns to scale seem to have
resulted in those markets with a relatively high share of
algorithmic trading activity also tending to see a concen-
tration of activity among a limited number of players
(Figure 3.8, panel 3). The high fixed costs associated
with internal development or deployment of sophisti-
cated AI let larger trading firms benefit from AI, while
they could lead smaller players to resort to critical third-
party service providers of cloud and AI software services,
further amplifying outsourcing, market concentration
and vendor lock-in risks (Figure 3.9).

Algorithmic trading now occupies a key role in
many capital markets, and its evolution is likely to
be driven by advances in AI. Strategies have already
evolved from relatively simple trading rules to more
complex algorithms and are now poised to use more
sophisticated AI. This will provide new competitive
advantages, primarily through the ability of AI to
process large amounts of high-frequency and unstruc-
tured data in short amounts of time and to extract
more value from it enabling automation of trading
decisions. Algorithmic trading has already fundamen-
tally altered the nature of capital markets, and the

86

International Monetary Fund | October 2024

CHAPTER 3  AdvANCES IN ARTIFICIAL INTELLIGENCE: IMPLICATIONS FOR CAPITAL MARkET ACTIvITIES

Figure 3.9. The Risks of Artificial Intelligence: Dependence on Third-Party Providers

IT infrastructure remains strongly concentrated, and the AI software
services market is becoming more concentrated.

There was longer outage time for IT infrastructure in 2023 than in 2022.

1. Market Share of Cloud Services and AI Software Platforms

(Percent; revenue in billions of US dollars)

2. Cloud Service Disruptions

(Hours per year)

80

60

40

20

0

Market share—No.3
Market share—No.2
Market share—No.1
Total revenue in billions of US dollars (right scale)

e
r
a
w

t
f
o
s

I
A

e
r
u
t
c
u
r
t
s
a
r
f
n

I

e
r
u
t
c
u
r
t
s
a
r
f
n

I

e
r
a
w

t
f
o
s

I
A

e
r
u
t
c
u
r
t
s
a
r
f
n

I

e
r
a
w

t
f
o
s

I
A

e
r
a
w

t
f
o
s

I
A

e
r
u
t
c
u
r
t
s
a
r
f
n

I

e
r
u
t
c
u
r
t
s
a
r
f
n

I

e
r
a
w

t
f
o
s

I
A

2019

20

21

22

23

150

120

90

60

30

0

2023 (205.3 hours)

2022 (133.5 hours)

160

120

80

40

0

Compute

Network

Storage

Database

Other
services

Sources: Bloomberg Intelligence; Parametrix Insurance; and IMF staﬀ calculations.

Note: In panel 1, infrastructure is represented by the aggregated revenue for the Infrastructure-as-a-Service segment, while AI software is represented by AI and predictive
analytics Platforms-as-a-Service segment. In panel 2, disruption events only include critical events as defined by Parametrix and cover total outage, severe performance
degradation, or other critical issues that require immediate action. As multiple groups may be aﬀected by the same event simultaneously, the sum of the duration from
each subcomponent is not equal to the total number of downtime hours. AI = artificial intelligence; IT = information technology.

finance literature has connected its history to provide
valuable insights into the potential changes to come:
 • Algorithmic trading is largely assessed to have a pos-
itive impact on market liquidity and efficiency, but
there may also be some negative impacts, especially
under stressed conditions. Research suggests that algo-
rithmic trading enhances liquidity and informational
efficiency, albeit at the cost of increased short-term
volatility (Hendershott, Jones, and Menkveld 2011;
Hendershott and Riordan 2012; Boehmer, Fong, and
Wu 2021). However, algorithmic trading can also
increase volatility following macroeconomic news and
can disincentivize informed traders from participat-
ing in the market, potentially even harming market
efficiency (Scholtus, van Dijk, and Frijns 2014; Yadav
2015). In the US Treasury market, one of the deepest
and largest markets in the world, digitalization has
dramatically improved liquidity on aggregate, but
this may have come at the cost of rare but extreme
bouts of illiquidity under stress (Bouveret and others
2015). Adrian, Fleming, and Vogt (2017) find that
market liquidity is affected by the extent to which
high-frequency traders are present in the market.

This is relevant from a systemic perspective because
most bonds are traded over the counter rather than
on centralized exchanges where banks and securities
dealers facilitate transactions.

 • Algorithmic trading could minimize price

swings that are not driven by new information
(Chaboud and others 2014). A decomposition of
high-frequency US stock returns into continu-
ous and “jump” components (Online Annex 3.1)
shows that idiosyncratic jumps in individual stock
returns—which could be evidence of a reduced
level of intermediation and lower liquidity—are less
and less frequent (Figure 3.10, panel 1).12 Fur-
ther analysis suggests that idiosyncratic jumps are
more frequent when liquidity conditions are poor
(Figure 3.10, panel 2). This substantiates the notion

12See Box 1.4 in the October 2018 Global Financial Stability
Report. Idiosyncratic jumps are identified by considering jumps in
individual stocks that do not coincide with jumps in large and liquid
passive ETFs that track the S&P 500 index (SPDR S&P 500 ETF
Trust—SPY). The robustness of this identification is tested by also
performing a jump decomposition of the residual stock returns, after
regressing out the index return.

International Monetary Fund | October 2024

87

GLOBAL FINANCIAL STABILITY REPORT: STEAdYING ThE COuRSE: uNCERTAINTY, ARTIFICIAL INTELLIGENCE, ANd FINANCIAL STABILITY

Figure 3.10. Algorithmic Trading and Market Efficiency

Markets have become less “jumpy” over the
past two decades ...

... whereby idiosyncratic jumps are associated
with illiquidity.

Liquidity and algorithmic trading may be
“flighty” under stress.

1. Stock Returns: Jump Frequencies and

2. Stock Returns: Idiosyncratic Jump

Share of Variance Explained
(Percent)

Frequency in Different Liquidity Conditions
(Percent)

3. Cancellation and Hidden Order Rates

(Ratio versus executed trades)

Jump frequency
Idiosyncratic jump frequency
Jump-driven share of variation
(right scale)

1.0

0.9

0.8

0.7

0.6

0.5

0.4

0.3

2007 09

11

13

15

17

19

21 23

16

14

12

10

8

6

4

2

Average jump frequency
Median jump frequency

1

2

3

4

5

6

7

8

9

10

Illiquidity (deciles by stock bid-ask spread)

0.65

0.60

0.55

0.50

0.45

25

20

15

10

5

0

Order cancellation rate
Hidden order rate

0–20 20–30 30–40 40–50 50–60 60–100
Implied volatility

Sources: Bloomberg Finance L.P.; US Securities and Exchange Commission, Market Information Data Analytics System; and IMF staff calculations.

Note: In panel 1, the now-standard thresholding approach and time of the day indicator in Bollerslev, Todorov, and Li (2013) are used to identify jumps. Data consist of
five-minute trading hour intraday data covering a balanced panel of 235 stocks between 2007 and 2024. In panel 2, the idiosyncratic jumps frequencies are computed per
stock-specific deciles of the daily bid-ask spreads. Higher deciles correspond to higher illiquidity. See Online Annex 3.1 for further details.

that algorithmic trading may have helped reduce
idiosyncratic jumps through its positive effect on
liquidity and market efficiency. AI-driven algorithms
could further facilitate this positive effect on market
stability.

 • Algorithmic risk limits may contribute to market
destabilization under stress. Algorithmic trading
strategies are often programmed to de-risk or even
shut down during periods of high volatility, partic-
ularly when faced with price signals that have not
occurred previously.13 These measures are intended
to protect individual trading firms from signif-
icant losses. However, under certain conditions
they could contribute to market destabilization
through a cascading and simultaneous triggering
of limits, feedback loops, and the sudden evapora-
tion of liquidity provided by algorithmic trading.
These AI-driven strategies may then be “switched
off.” Data from US equity markets provide some
evidence for the notion that liquidity provided
by algorithmic trading diminishes under stress.

13These limits can include restrictions on the total volume of
trades, maximum loss thresholds, or limits on exposures to specific
assets or markets. A survey of energy traders conducted by the Dutch
Authority for Consumers and Markets found that algorithms are
subject to position limits (14/15), price limits (13/15), volume limits
(12/15), and other limits (ACM 2024).

High-frequency traders often make use of order
cancellations (Weller 2017), but order cancellation
rates drop significantly as implied volatility increases
(Figure 3.10, panel 3). Simultaneously, hidden order
rates increase. Hidden orders are typically used by
large institutional investors to minimize the market
impact of their trades when liquidity is limited.
Both observations are consistent with the concept
of “flighty liquidity-under-stress.” Based on feed-
back received during the IMF outreach, AI-driven
algorithmic trading strategies are also subject to the
same measures under stressed conditions, especially
when regular and predictable market patterns break
down.

 • GenAI could facilitate the proliferation of algo-
rithmic trading across new asset classes, trading
venues, and geographic regions.14 GenAI can
lower barriers to entry for algorithmic trading,
as it facilitates coding, testing, and automation
of trading in less technologically sophisticated
trading venues. It could also help mitigate some
of the obstacles that have previously impeded the
proliferation of algorithmic trading. For example,
in asset classes with highly diverse instruments
(for example, corporate bonds) that do not

14See, for example, London Stock Exchange Group 2024.

88

International Monetary Fund | October 2024

CHAPTER 3  AdvANCES IN ARTIFICIAL INTELLIGENCE: IMPLICATIONS FOR CAPITAL MARkET ACTIvITIES

Table 3.1. Potential Impact of the Adoption of Artificial Intelligence in Algorithmic Trading

Market liquidity

Negative Scenario

Positive Scenario

AI magnifies existing risks related to algorithmic trading
by facilitating its growth. AI could “democratize” and
expand algorithmic trading activity to a broader set of assets
and geographic areas. This could exacerbate risks related to
sudden liquidity withdrawal under stressed conditions.

AI increases the stability of algorithmic trading under stressed
conditions. AI-driven algorithms could operate in a wider set
of market conditions than traditional algorithms, with lower
flash-crash risk, and reduced liquidity-withdrawal under stress.

Leverage

AI-driven strategies boost short-term leverage. As arbitrage

opportunities are exploited more efficiently by more
advanced algorithms, remaining opportunities might require
higher leverage to deliver similar returns.

AI improves the management of leverage and related risks.
AI could facilitate more frequent and automated management
of leveraged positions, based on more inputs, and mitigate
operational lags.

Interconnectedness

AI increases interconnectedness. AI could proliferate algorithmic trading to other asset classes, geographic regions, and trading
venues, and also operate in between different market segments; that is, in a multi-asset and multitrading venue approach.

Increased interconnectedness leads to higher correlations
between capital market segments, facilitating spillovers
and transmission of stress.

Market access, efficiency, and liquidity improve for some market

segments, including emerging markets.

Source: IMF staff assessment.
Note: AI = artificial intelligence.

naturally lend themselves to automated trading,
GenAI can facilitate the processing of complex
text-based data (such as bond indentures) to
enable more standardized risk analysis, and pric-
ing tools can support liquidity.15

On balance, the impact of these changes from a
financial stability perspective is highly uncertain. Given
the nascent nature of the use of AI in algorithmic
trading, multiple scenarios could materialize. Table 3.1
outlines the potential positive and negative scenarios
related to liquidity, leverage, and interconnectedness in
financial markets.

New Dynamics That Could Be Driven by
Further Adoption of Artificial Intelligence

Beyond these traditional risk areas, AI could create

new market dynamics and new risks to financial
stability:
 • AI-driven strategies could drive higher and more

procyclical trading volumes. AI can quickly process
vast amounts of new information and may therefore
spur larger and more frequent portfolio adjustments,
leading to higher trading volumes. Portfolio turn-
over for AI-powered ETFs16 provides evidence for

15Examples of AI-driven tools in bond markets include Overbond

(https://overbond.com/) and BondGPT (https://www.ltxtrading.
com/bondgpt).

16AI-powered ETFs are ETFs whose security selection and weights

are optimized and periodically rebalanced using AI techniques with
the objective to outperform their respective benchmarks.

this scenario. ETFs with AI-driven strategies have
experienced significantly higher turnover than other
active or passive ETFs (Figure 3.11, panel 1), whose
turnover has been relatively stable or slightly declining
in recent years.17,18 These higher trading volumes not
only can enhance price discovery during stable market
conditions but also can contribute to market instabil-
ity in times of stress. Three sample AI-driven ETFs
increased their portfolio turnover during the March
2020 market turmoil, providing some evidence for
procyclicality (Figure 3.11, panel 2).

 • Markets could react faster to news. There is some

evidence of higher-speed adjustment based on an exam-
ination of historical releases of the Federal Open Mar-
ket Committee minutes, usually a complex and lengthy
document. Intraday market data suggest that, after the
introduction of large language models, the initial mar-
ket reaction following the release of the minutes (up to
45 seconds) tends to reflect its eventual impact more
accurately than in the period before the introduction of
these technologies (Figure 3.11, panel 3).

 • AI algorithms could collude or manipulate markets.
Risks in this area are currently being investigated
through theoretical models of potential interactions

17Bonelli and Foucault (2023) find that big data allows active asset
managers to find new trading signals but that doing so requires new
skills. Thus, big data can reduce the ability of asset managers lacking
these skills to produce superior returns, and it has the potential to
displace high-skill workers in finance.

18Chen and Ren (2022) find that AI-powered mutual funds do
not outperform the market but that they do significantly outperform
their human-managed peers through superior stock selection capabil-
ity and lower turnover ratios.

International Monetary Fund | October 2024

89

GLOBAL FINANCIAL STABILITY REPORT: STEAdYING ThE COuRSE: uNCERTAINTY, ARTIFICIAL INTELLIGENCE, ANd FINANCIAL STABILITY

Figure 3.11. New Artificial Intelligence Trading Dynamics

The annual turnover of AI ETFs outstrips that of other active ETFs and has
been increasing.

AI-driven trading could drive higher trading volumes, especially during
periods of volatility.

1. Portfolio Turnover of ETFs

(Percent of assets under management)

2. Gross Turnover and Implied Volatility Index

(Percent of assets under management at end of month; implied volatility
index, right scale)

10,000

1,000

100

10

1

FY2019

FY2020

FY2021

FY2022

FY2023

Passive

Active: Factor

Active: Thematic

AI

150

100

50

0

ETF A
ETF B
ETF C
VIX
(right scale)

Jan. 2020

Feb. 20

Mar. 20

Apr. 20

May 20

Jun. 20

75

60

45

30

15

0

Markets may already be reacting faster and more accurately to textual
data releases.

Theoretical models point to various possible market structure outcomes,
including algorithmic manipulation.

3. S&P 500 Index Normalized Market Reaction after Release of Federal

4. Market Ineﬃciency for AI Traders Who Are Learning under

Open Market Committee Minutes
(Proportion of price change at the 15-minute point)

Information Asymmetry
(Percent)

0.4

0.2

0

−0.2

Interquartile range (before LLMs)
Interquartile range (after LLMs)
Median (before LLMs)
Median (after LLMs)

Information advantage (gap):

0 percent
50 percent
100 percent

0–15 sec. 15–30 sec. 30–45 sec. 0–15 sec. 15–30 sec. 30–45 sec.

1

2

Before LLMs

After LLMs

4

6
Number of underinformed agents

8

10

12

70

60

50

40

30

20

10

0

)
p
a
g
e
c
i
r
p
(
y
c
n
e
i
c
ﬃ
e
n

i

t
e
k
r
a
M

Sources: Bloomberg Finance L.P.; US Securities and Exchange Commission filings; and IMF staﬀ calculations.

Note: In panel 2, ETF A, B, and C are samples of three AI-powered EFTs operating during the period. The implied volatility index is the VIX index. In panel 3, events have
been included if the index price change surpassed 0.2 percent at the 15-minute mark. The periods before and after LLMs are separated by the publication of Vaswani and
others (2017), which introduced the attention model and transformer architecture. Both the before and after LLM subsamples contain 15 qualifying datapoints. Panel 4
shows simulated scenarios from Fan, Pelger, and Yu (forthcoming), where deep learning-based reinforcement learning agents with diﬀerent signals trade with each other.
The panel displays the price gap between the market price and fundamental value. AI = artificial intelligence; ETF = exchange-traded fund; FY = financial reporting year;
LLM = large language model.

90

International Monetary Fund | October 2024

CHAPTER 3  AdvANCES IN ARTIFICIAL INTELLIGENCE: IMPLICATIONS FOR CAPITAL MARkET ACTIvITIES

between AI trading algorithms. Such models show a
variety of different possible outcomes. In some cases,
tacit algorithmic collusion could emerge (Dou,
Goldstein, and Ji 2024). By contrast, the empirical
literature points to the possibility of a “winner takes
all” scenario (Baron and others 2017), which could
result in market inefficiency—measured by the
price gap between market prices and fundamental
values—and manipulation. Manipulation is more
likely if one algorithm has either an information or
a latency advantage, and when the market has fewer
players (Figure 3.11, panel 4).19

19Figure 3.11, panel 4, shows simulated scenarios from Fan, Pel-
ger, and Yu (forthcoming). The panel displays the price gap between
the market price and fundamental value based on a simulated
market with informed and uninformed algorithmic traders that learn
from each other’s actions. One informed reinforcement learning
agent holds one-eighth of the total market buying power, while the
remaining buying power is evenly split among varying numbers of
uninformed reinforcement learning agents. When the number of
uninformed agents increases, it becomes harder for the informed
reinforcement learning agent to manipulate the price, and hence,
the equilibrium price gets closer to the fundamental value. The sce-
nario with two uninformed agents makes it most likely to generate
self-perpetuating trends, which are initiated by the informed rein-
forcement learning agent and take the form of local price bubbles.

Financial Stability Implications
Market Participants Are Most Worried about
Concentration Risk

Participants in the IMF outreach cited potential
herding and market concentration as a key financial
stability risk that could result from wider and con-
tinued adoption of AI models in capital markets,
especially those working at market infrastructure
providers, assets managers, and academia (Figure 3.12,
panel 1). This concern was viewed as especially
pertinent if trading and investment strategies were
to become largely derived from open-source AI and
trained on similar data sourced from the same set of
vendors. Correspondingly, vendor concentration was
also viewed as a potential source of systemic risk, as
overdependence on a limited number of AI model
providers and data vendors could lead to mass disrup-
tions to trading and investment were one or some of
these vendors to fail.

Participants in the outreach also saw a possibility
for widespread adoption of AI to introduce market
manipulation (for example, through deepfakes or
misinformation). Some participants mentioned market
fragility issues—including the drying up of market

International Monetary Fund | October 2024

91

Figure 3.12. Market Intelligence: Risks and RegulationSome of the largest risks involve herding and market concentration as well as model explainability.1. What Risks Are Associated with Using Generative AI?(Share of participants)Most market participants agree that regulators should ensure market integrity through monitoring and maintain human oversight of decision making.2. How Do You Expect Regulatory Authorities to Respond to the Risks of Generative AI?Sources: IMF, October 2024 Global Financial Stability Report market intelligence; and IMF staﬀ calculations.Note: For both panels, deepfake risks are included in the cyber categories, and additional information on market intelligence can be found in Box 3.1. In panel 1, the size and color of the bubbles represent the share of participants. Panel 2 shows that industry market participants expect regulatory authorities to intervene to limit the risks of generative AI. Infrastructure refers to market infrastructure firms. Other industry types in panel 2 include AI vendors, academia, and rating agencies. AI = artificial intelligence; EMDE = emerging market and developing economies.Herding and market concentrationVendor concentrationMarket fragility and manipulationCyber risksJob replacementModel explainabilityHallucinationsHigh costsUnauthorized dataReputational risksEMDE fragmentationKey RisksOther RisksMarket integrity and monitoringHuman oversightTransparency and disclosuresManaging model riskBalance consumer protections and innovationEnhance regulatory capacityCyber resilienceBank/dealerAsset managerInfrastructureOtherGLOBAL FINANCIAL STABILITY REPORT: STEAdYING ThE COuRSE: uNCERTAINTY, ARTIFICIAL INTELLIGENCE, ANd FINANCIAL STABILITY

liquidity, excess volatility, and flash crashes—arising
from fast-paced decision making and ineffectiveness
of guardrails that may result, for instance, from the
poor design of such guardrails, the growing complexity
of the AI system, or even a malicious intervention.20
Other participants viewed threats such as cyberattacks
on financial intermediaries and market utilities, and
large-scale data poisoning as a potential source of
systemic risk (Box 3.2). To a lesser extent, the acquisi-
tion of data scientists and other professionals that can
work in an AI-driven environment was also raised as a
concern.

Some participants raised concerns that the lack of
model explainability and model hallucination21 could
be detrimental to trust in markets. Others expressed
concern over high costs associated with fine-tuning
sophisticated models using large data sets creating
potential for an unlevel playing field, with large firms
having an advantage. Few participants also worried
that customer fraud, unauthorized use, and data access
could pose risks and compliance issues, leading to
reputational damage. To a lesser extent, the adoption
of AI could exacerbate spillovers of advanced economy
shocks to emerging market and developing econo-
mies,22 particularly if AI models are more sensitive
to price fluctuations and managed against a basket of
various asset classes. Alongside increasing transactions
and sensitivity to market news, cross-border capital
flow volatility could also increase and be destabilizing,
particularly for relatively smaller and less liquid mar-
kets with largely fragmented participants.

20Guardrails refer to various microstructure mechanisms (such
as pretrade controls, circuit breakers, volatility parameters, and kill
switches). Issues with participant systems may impact a trading
venue’s ability to maintain a fair and orderly market. This might
necessitate a trading venue to introduce microstructure mechanisms
and tools to manage these risks and address the issues that arise. For
details, see IOSCO (2015).

21See Shabsigh and Boukherouaa (2023, p. 7), who explain
how “GenAI’s ability to generate new content based on training
data comes with the risk that GenAI models could produce wrong
but plausible sounding answers or output and then defend those
responses confidently—a phenomenon broadly referred to as
‘hallucination.’”

22A potential AI use case for emerging market and develop-
ing economy assets is on managing foreign exchange risk. Some
corporate treasurers are experimenting with AI techniques to
assess currency risk exposure, predict market trends, and cal-
culate optimal foreign-exchange hedging ratios. See Lipsky,
Cole. 2024. “Banks, Vendors Mine AI for Corporate FX Hedg-
ing.” Risk.net, June 6. https://www.risk.net/markets/7959503/
banks-vendors-mine-ai-for-corporate-fx-hedging.

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International Monetary Fund | October 2024

Regulators Are Expected to Enhance Monitoring
and Provide Guidance on the Risk Management
of Artificial Intelligence Models

In response to the growing uncertainty and risks
emerging from the adoption of AI/ML, participants
in the IMF outreach expected regulatory authori-
ties to provide clarity and guidance on model risk
management, emphasize stress testing for extreme
scenarios, and provide transparency and clearer
disclosures (Figure 3.12, panel 2). Stakeholders also
anticipated guidance on industry-specific regulatory
structures to avoid violation of existing regulations,
guidelines on AI use in consumer-facing applications,
and accountability frameworks. Both buy-side and
sell-side entities, along with academia and market
infrastructure providers, emphasized the need for
balanced regulation that ensures responsible use of
AI without stifling innovation while at the same time
ensuring adequate consumer protection. There was
consensus that capital market supervisors should focus
on providing guidelines and best practices rather than
strict rulemaking, given the rapidly evolving nature of
AI technology in financial markets. Some participants
noted the importance of addressing bias in AI models
and the potential need for supervisors to ensure better
AI preparedness through continuous upskilling while
integrating AI/ML (including sophisticated AI) in
their supervision and market surveillance functions.
Their overall sentiment was that the regulatory
approach should be flexible and adaptable to keep
pace with the rapid advancements in AI technology in
the financial sector.

Summarizing the Financial Stability Challenges:
Current and Prospective

The use of AI in capital markets is still relatively

nascent, and currently the financial stability risks
associated with its adoption appear contained. Even
so, there are already well-documented instances of
sophisticated AI being used to generate disinfor-
mation with the goal of manipulating markets, and
more importantly, more malicious cyber threats (see
Box 3.2).

The analytical work and the market participant
responses to the IMF’s outreach documented in this
chapter demonstrate that rapid adoption of AI in
capital markets is likely and that it may drive some

CHAPTER 3  AdvANCES IN ARTIFICIAL INTELLIGENCE: IMPLICATIONS FOR CAPITAL MARkET ACTIvITIES

transformative impacts on markets that lead to several
financial stability challenges:
 • Increased market speed and volatility under stress
 ◦ Continued growth of AI-enhanced algorithmic
trading strategies could enhance market liquid-
ity and bring efficiency gains, manifesting in
the form of more prompt price adjustments in
response to new information and also thinner
margins for traders. But both could incentivize
an increased use of leverage across the financial
system and result in more amplification between
falling asset prices, volatility, and deleveraging in
periods of stress.

 ◦ AI models may herd and produce rather similar
decisions, especially during stress periods result-
ing in procyclical financial stability risks. During
normal times, AI models may uncover new trading
opportunities, leading to more diverse investment
strategies that would be positive for financial market
resilience. During adverse shocks, however, models
could simultaneously rebalance portfolios toward
safe assets, creating a self-fulfilling spiral of fire sales.

 ◦ Novel adverse events—such as the COVID-19
 pandemic in 2020—may drive AI model out-
comes that are difficult to comprehend, or models
may simply shutdown, requiring humans to make
decisions on and process a voluminous number of
trades. This vulnerability could be more heightened
if AI trading algorithms collude with each other,
resulting in a winner-dominated market that could
be more easily upended by adverse shocks.

 • More opacity and monitoring challenges

 ◦ AI may spur further migration of activities to

NBFIs. Since the global financial crisis, trading
and investment activity, and especially capital
market activities, have steadily migrated out of
the banking sector and into NBFIs (see Chapter 2
of the April 2023 Global Financial Stability
Report). Some NBFIs have now built extensive
expertise and technology to help them take
advantage of new advances in AI. Regulatory
requirements for banks regarding the explainabil-
ity and transparency of internal models—com-
pared to comparatively lighter requirements for
NBFIs—give NBFIs a competitive advantage over
banks in reaping the benefits of complex models,
thereby raising systemic opacity.

 ◦ AI models could generate portfolios across
different asset classes, geographic regions,

and trading venues, creating correlations and
interconnectedness that are not relevant at the
current juncture. This could undermine the
ability of regulators to monitor financial risks
holistically.

 ◦ There will likely be emergent, new forms of risks
(for example, potential complex interactions
between autonomous AI agents not visible at the
level of individual institutions or at the regulatory
level).

 • Increased operational risks as a result of reliance
on a few key third-party AI service providers
 ◦ AI models and related information technology
services currently reside with a handful of key
providers with dominant computational power
and established large language models. If capital
markets activities become too reliant on these
models, the failure of these providers may lead to
market stress akin to the failure of key financial
market utilities such as clearing houses.
 • Increased market manipulation and cyber risks

 ◦ Fraud, disinformation, and deepfakes will likely
become more sophisticated as AI advances and
could be used by bad actors to manipulate finan-
cial markets and asset prices.

 ◦ Data integrity and confidentiality could be

compromised, leading to AI models producing
suboptimal trading and investment decisions.

Regulatory and Supervisory
Developments
International, National, and Supervisory
Artificial Intelligence Initiatives

International organizations, standard-setting bodies,

and financial sector authorities for larger capital
markets have identified the use of AI/ML by market
intermediaries and asset managers as a key priority,
given the cautious but steady pace of its uptake. As
capital markets are already subject to regulation and
supervision, institutions are responsible for AI systems
they deploy, whether internally developed or externally
sourced. Existing regulatory and supervisory frame-
works for capital markets are largely technology-neutral
and are also applicable to AI systems. Ongoing work
by financial sector authorities explores and provides
guidance on application of existing prudential frame-
works as well as the need for additional frameworks
to effectively cover the risks specific to the use of AI,

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GLOBAL FINANCIAL STABILITY REPORT: STEAdYING ThE COuRSE: uNCERTAINTY, ARTIFICIAL INTELLIGENCE, ANd FINANCIAL STABILITY

Figure 3.13. Standard Setter, National, and Supervisory Artificial Intelligence Initiatives

Standard-setting bodies are designing
frameworks by building on accepted practices,
while considering their application to AI.

Governments with large capital markets are
leveraging cyber and data frameworks to pivot
to AI strategies ...

... whereas financial supervisors are cautious
and respond through targeted outreach and
clarification of existing standards.

1. Standard-Setting Body Response

2. National Response

(Percent)

3. Supervisory Response

(Percent)

Outsourcing

Data quality
and bias

Governance of
AI use

Key Areas

Transparency
and
explainability

Undertaken

No action yet

Undertaken

No action yet

AI Act

67

Enforcement

4

National strategy

79

Principle based

12

Need for
a human-in-
the-loop

Data governance
and protection

Reporting
requirements

Cybercrime

96

96

Clarification

65

Outreach

69

Sources: Panel 1: IMF staff literature review of international standards and regulatory and supervisory publications. Panels 2 and 3: UN Institute for Disarmament Research
AI Policy Portal; UN data protection and privacy legislation worldwide; and IMF staff analysis and review of AI-related initiatives taken by financial sector authorities.

Note: The analysis covers the jurisdictions with stock exchange operators with a market capitalization of listed companies exceeding $1 trillion as of March 2024. The
sample consists of 24 jurisdictions, of which 11 are members of the European Economic Area, and 26 financial market authorities, of which 11 are from the European
Economic Area and 3 are from the United States. AI = artificial intelligence.

which so far focus more on conduct issues such as
ethics, fairness, and transparency.

Most current AI initiatives by standard-setting

bodies begin by saying that financial sector authorities
should remain vigilant on AI deployment by capital
market participants and be prepared to respond to
an acceleration in the pace of adoption. It is recom-
mended that financial sector authorities update their
skills and supervisory tools to monitor more complex
investment strategies and process more granular data
in real time. In addition, financial sector authorities
should proactively question whether extant regulatory
frameworks adapt to novel forms of AI with a compre-
hensive view of emerging risks.

In this context, standard-setters and financial sector

authorities have issued or are revisiting assessments
(FSB 2017b), guidance, and regulatory frameworks
that take into account the various risks of AI deploy-
ment (Figure 3.13, panel 1) in a number of key areas.
Existing frameworks issued by the Financial Stability
Board (FSB), Bank for International Settlements, and
national regulators address financial stability, market
integrity, and investor protection concerns mostly

by building on the principles of technology-neutral,
results-based, and proportional regulation and
supervision (Monetary Authority of Singapore 2018;
Hong Kong Monetary Authority 2019).23 The FSB
issued guidance for managing third-party risk and
cyber incidents (FSB 2020, 2023a). The Basel Com-
mittee frameworks for banking institutions that
participate in capital markets include principles and
recommendations on data governance and opera-
tional and cyber risk management (BCBS 2013; BIS
2023). The US National Institute of Standards and
Technology (NIST) has recently issued a relevant AI
framework (NIST 2024). The International Orga-
nization of Securities Commissions (IOSCO) has
addressed algorithmic trading and market volatility
(IOSCO 2018) and AI risks in market intermediaries

23While issued by the US Executive Power, the Executive Order
on the Safe, Secure, and Trustworthy Development and Use of Arti-
ficial Intelligence (https://www.whitehouse.gov/briefing-room
/presidential-actions/2023/10/30/executive-order-on-the-safe-secure
-and-trustworthy-development-and-use-of-artificial-intelligence/)
encourages independent regulatory agencies to consider using their
authority to prevent discrimination and address risks arising from
the use AI to financial stability.

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International Monetary Fund | October 2024

CHAPTER 3  AdvANCES IN ARTIFICIAL INTELLIGENCE: IMPLICATIONS FOR CAPITAL MARkET ACTIvITIES

and asset management (IOSCO 2021). Currently,
IOSCO is conducting a two-year project to assess
risks and challenges associated with the use of AI, with
potential policy guidance expected by the first quarter
of 2025 (IOSCO 2024).

An IMF review of actions taken by 26 authorities
in large capital markets finds that governments have
already begun to formulate comprehensive AI strategies
and act in the areas of data protection, governance,
and cybercrime (Figure 3.13, panel 2). Some juris-
dictions are also considering dedicated AI legislation
to ensure robust governance for this rapidly evolving
technology. However, supervisory authorities remain
cautious in this area, and so far, have focused primarily
on clarification and outreach, rather than on enforce-
ment (Figure 3.13, panel 3).

Best Practices

Given the rapidly evolving and uncertain landscape

of AI in capital markets, engagement through out-
reach is crucial. Establishing public/private forums to
develop overarching principles (Office of the Super-
intendent of Financial Institutions 2023), partnering
with the industry to build a risk framework (Mone-
tary Authority of Singapore 2024), and conducting
surveys on the applicability of existing frameworks are
mechanisms that can be conducive to a safe adop-
tion of AI (US National Archives 2021; Institute for
Workplace Equality 2022; Bank of England 2024).
Engagement also helps financial sector authorities
assess whether existing risk management guidance
takes into account the specific challenges of AI
models, namely explainability, robustness, data bias/
privacy, and cybersecurity, and to what extent AI
is being used in the sector and for which particular
services and activities. Other practices within the
banking sector relate to requesting notification by
banks prior to their adoption of certain technologies
or arrangements with third parties (BCBS 2024).

AI is providing numerous opportunities for super-
visors to generate efficiency gains by automating data
quality checks to ensure completeness, correctness,
and consistency. AI can also combine multiple data
sources, even when original data lack a unique
identifier, and help financial sector authorities detect
anomalies in trading patterns, reflected in changes in
prices, volume, and volatility (di Castri and others
2019). Other applications may aim to identify mis-

leading information or perform real-time monitoring
of market transactions. GenAI offers new possibilities
to financial sector authorities because it enhances
information retrieval, content creation, and code gen-
eration, debugging, and explanation, as well as legacy
code optimization. These tools could enable financial
sector authorities to accelerate the deployment of
more traditional use cases such as fraud detection
or monitoring of market activity, or streamline data
management tasks.

While the adoption of new and emerging technology
for supervisory processes (known as SupTech) continues
to trend upward, the adoption rates between advanced
economies and emerging market and developing econ-
omies are uneven (Cambridge SupTech Lab 2023).24
Periodic upskilling and upgrading should help financial
sector authorities identify AI use–specific issues like
models designed to “game the regulation” and detect
algorithmic coordination. Finally, existing cross-sectoral
thematic reviews could reveal potential herding or
material interconnectedness among market participants
and also help identify best practices in the use of AI
(Securities and Exchange Board of India 2019).

Policy Recommendations

Regulation and supervision in AI-related areas
should be enhanced to address potential financial
stability risks for both the banking and NBFI sec-
tors. Regulatory and supervisory frameworks should
follow a balanced approach, allowing financial sector
participants to reap the potential benefits of AI while
acknowledging its risks (IMF and World Bank 2018).
Across sectors, supervisors should continue to strive for
cyber resilience and address dependency on data, mod-
els, and third-party service providers by requesting risk
mapping. Specific to capital markets, areas that could
be strengthened further relate to over-the-counter
markets and existing measures to address volatility.
Implementation of these recommendations will require
regulatory reporting to allow for continued structural
assessment of the developments and accompanying
risk, which is more achievable with an outreach or
survey approach.

24In 2023, 79 percent of advanced economies and 54 percent of
emerging market and developing economies had adopted SupTech
tools, compared to 50 percent and 31 percent, respectively, in 2022.

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GLOBAL FINANCIAL STABILITY REPORT: STEAdYING ThE COuRSE: uNCERTAINTY, ARTIFICIAL INTELLIGENCE, ANd FINANCIAL STABILITY

Address Increased Market Speed and Volatility
under Stress

Financial sector authorities and trading venues

should determine whether designing new or modifying
existing volatility response mechanisms is necessary
to respond to crash events potentially originated in
AI-driven-trading. Existing circuit breakers may need
to be re-parameterized in light of changing market
structures. However, poorly designed circuit breakers
may exacerbate volatility and interfere with market
efficiency and price discovery (Vereckey 2023). Testing
algorithms in controlled environments could help
financial sector authorities, trading venues, and market
actors assess their behavior in extreme circumstances.
Financial sector authorities, trading venues, and
central counterparties should review margining require-
ments and other buffers in light of potentially rapid
AI-driven price moves. In line with policy proposals
by the Basel Committee on Banking Supervision, the
Bank for International Settlements’ Committee on
Payments and Market Infrastructures, and IOSCO,
further international work is needed to (1) foster mar-
ket participants’ preparedness for the large variation
margin calls that can occur during market stress; (2)
identify good practices for variation margin collec-
tion and distribution by the central counterparty;
(3) understand the degree and nature of the central
counterparty margin models’ responsiveness to vola-
tility and other market stresses; and (4) review initial
margin levels in non-stress times, including a review of
the effectiveness of tools to reduce the procyclicality of
margin models (BCBS, CPMI, and IOSCO 2022).

Address Increased Opacity and
Monitoring Challenges

Financial sector authorities should ask financial

institutions to regularly map interdependencies between
data, models, and technological infrastructure support-
ing AI models.25 These models may feed on shared or
interdependent data sources; share a common archi-
tecture; and rely on a small number of providers for
software, data, and cloud services. In addition, data sets
may not cover a complete financial cycle, undermining
the reliability of models built upon them (Gensler and
Bailey 2020). Although regulatory frameworks require
assessing the cumulative effects of models, they do not

25Similar to, but expanded to data and AI systems, see Principle 4

of the BCBS “Principles for Operational Resilience” (BCBS 2021).

96

International Monetary Fund | October 2024

mandate a joint assessment of data dependencies. An
updated view of these interdependencies will enable
financial sector authorities to proactively manage risks
and promote a resilient ecosystem.

Financial sector authorities should continue to
strengthen their oversight and regulation of NBFIs
by requiring them to identify themselves and disclose
AI-relevant information. The authorities could monitor
the activity of market participants that conduct a
substantial amount of trading activity (“large traders”)
(IOSCO 2011). Each such large trader should be
uniquely identified and provide information on its
activities to its registered broker-dealer in its securities
market, which would allow for monitoring by finan-
cial sector authorities. Other measures, such as those
proposed by the FSB and IMF, and which also address
risks of further adoption of AI in the NBFI sector,
should aim to continue strengthening resilience there
(see Chapter 2 of the April 2023 Global Financial Sta-
bility Report; FSB, n.d.). On that point, enhancing risk
management and strengthening liquidity buffers could
contribute to the resilience of NBFIs, thereby mitigat-
ing the effects of asset mispricing or liquidity runs.

Address Increased Operational Risks as a Result
of Reliance on a Few Key Third-Party Artificial
Intelligence Service Providers

Financial sector authorities should undertake a coor-
dinated approach to the regulation and supervision of
AI service providers. To this purpose, it is crucial to map
the relationships and correspondences between critical
AI service providers and essential IT infrastructure pro-
viders Failure or disruption of critical third parties may
affect the stability of and confidence in the financial sec-
tor (Federal Reserve System, Federal Deposit Insurance
Corporation, and Department of the Treasury 2023; see
also European Securities and Markets Authority 2023).
Comparable and interoperable regulatory approaches to
critical service providers facilitate compliance across the
financial sector and coordination among financial sector
authorities (FSB 2023b). The authorities should also
ensure that the definition of critical service providers is
broad enough to capture the systemic use of common
AI models (Bank of England 2024).

Financial sector authorities should continue to strive
for resilience in capital markets by requiring protocols
to avoid, protect against, respond to, and recover from
attacks. AI systems are exposed to various types of
attacks that can affect both the data used to train the

CHAPTER 3  AdvANCES IN ARTIFICIAL INTELLIGENCE: IMPLICATIONS FOR CAPITAL MARkET ACTIvITIES

algorithm and the model itself, and that aim to either
manipulate model results or extract their coding. AI
systems are like other information technology systems,
so cybersecurity needs should be contemplated at var-
ious stages—namely the design, development or pro-
curement, deployment, and operations stages (National
Cyber Security Centre 2023).

Address Over-the-Counter Monitoring Needs and
Resilience Risks

Financial sector authorities should be prepared to
adopt measures that ensure continued market integ-
rity, efficiency, and resilience of over-the-counter

markets when AI use proliferates. The authorities
should consider collecting and disseminating more
detailed information on over-the-counter transac-
tions, requiring market participants to account for
liquidity shifts in their risk management frame-
work, establishing or expanding existing incentives
for market-makers to enhance liquidity, improving
incentives for central clearing, and establishing
margin requirements for non-centrally cleared deriv-
atives. In the event of shocks, backstop measures
could include central bank liquidity provision to
market-making banks or indirectly support non-
bank dealers by easing market funding conditions
(CGFS 2014).

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Box 3.1. IMF Staff Market Outreach: Qualitative Assessment of Advances in
Artificial Intelligence and Its Implications for Capital Markets

IMF staff conducted extensive outreach across
various stakeholders to gather market intelligence on
how financial institutions—both buy-side and sell-side
firms—are harnessing advances in artificial intelligence
(AI) for capital market activities, and on the potential
impact of AI adoption. The objective was also to gain
a forward-looking view on how the rise of sophisti-
cated AI (including generative AI) technologies might
influence financial activity in the future, especially in
terms of the use of AI and machine learning (ML) for
asset allocation and trading.

IMF staff engaged in a large number of meet-
ings with bank/dealers, AI vendors, asset managers,
academia, rating agencies, and market infrastructure
firms, among others (Figure 3.1.1), and received
detailed responses from 27 stakeholders directly
involved in AI topics and business. While buy-side
firms include asset managers, mutual funds, hedge
funds, pension funds, private equity firms, and insti-
tutional investors, sell-side firms consist of investment
banks, brokerage firms, market makers, and research
analysts. Bilateral discussions focused on the use of
AI/ML, including sophisticated AI for investing across
various asset classes, expected use cases and benefits
in investment and trading decisions, prospects around
AI-based trading autonomy, risks and challenges
(including potential systemic risks), and expected reg-
ulatory guidance on AI deployment. The outreach also
sought feedback on the potential impact on emerging
market and developing economies in terms of capital
flows and potential fragmentation risk.

Given the challenge to identify a homogenous

definition of AI and distinguish AI/ML from sophisti-
cation AI, IMF staff adopted the following definitions:
“AI/ML models” referred to well-established predic-
tive analytics, including neural networks, clustering
algorithms, natural language processing, decision trees,
and so on; and “sophisticated AI models” referred to

Figure 3.1.1. Participants in the IMF’s
Market Intelligence Outreach
(Percent)

Bank/dealers
Market infrastructure
Others

Asset managers
Academia

20

28

28

12

12

Sources: IMF, October 2024 Global Financial Stability
Report market intelligence; and IMF staff calculations.
Note: “Others” includes nonprofit financial organizations,
artificial intelligence finance conferences, artificial
intelligence vendors, and rating agencies. This figure does
not include regulatory outreach.

the latest innovations, such as deep learning, rein-
forcement learning, and large language models. This
includes generative AI models capable of generating
text, codes, images, and other content. For certain
topics for which questions can be asked consistently
across participants (for example, asset classes for which
AI is used, key risks that are top of mind), staff tabu-
lated the results of answers to the questions.

The IMF’s outreach was accompanied by an exten-

sive literature review, data collection, and analytical
work. In parallel, staff conducted a regulatory outreach
with 10 capital market supervisors of advanced and
emerging markets.

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CHAPTER 3  AdvANCES IN ARTIFICIAL INTELLIGENCE: IMPLICATIONS FOR CAPITAL MARkET ACTIvITIES

Box 3.2. Manipulation and Cyber Risk: The Artificial Intelligence Arms Race

Generative artificial intelligence can be used by

bad actors to manipulate markets or to conduct
cyberattacks. Cyberattacks have been on the rise in
recent years (Figure 3.2.1, panel 1), with the share
of attacks on finance and insurance sector entities
more than doubling over the past decade. Previous
IMF work has shown one measure of potential
maximum annual financial firm losses from cyber
incidents has increased from $300 million to
$2.2 billion since 2017 (see Chapter 3 of the April
2024 Global Financial Stability Report).1 Cybercrim-
inals can produce deepfakes, manipulating audio
and video to impersonate key individuals in the
financial sector, or spread other misinformation.
Such deepfakes can lead to fraudulent transactions,

manipulated stock prices, or an erosion of trust in
financial institutions, triggering selloffs or deposit
runs. Critical financial market or information
technology infrastructure can be targeted, leading
to significant disruptions in financial markets and
beyond.

Although a dedicated AI cyberattack database

does not currently exist, AI incidents are being
tracked by multiple databases.2 Despite better AI
preparedness, most AI incidents have occurred in
advanced economies, even when accounting for dif-
ferences in GDP (Figure 3.2.1, panel 2). In many
cases, AI incidents concern cases where AI was used
with a legitimate objective, but where unanticipated
consequences appeared. AI incident rates therefore

Figure 3.2.1. Cyberattacks and Artificial Intelligence Incidents

Cyberattacks have increased, with the financial sector share
rising as well.

Despite higher AI preparedness, North America and
Europe have higher rates of AI incidents.

1. Cyberattacks

2. AI Incidents per Unit of GDP and AI Preparedness

(Number per year; finance and insurance sector share in percent)

(Number per trillions of US dollars; index)

3,000

2,500

2,000

1,500

1,000

500

0

Finance and insurance
Finance and insurance share (right scale)

All other sectors

14

12

10

8

6

4

2

0

Asia-Pacific

North America

Euro area

Middle East and
Central Asia

Sub-Saharan Africa

Latin America and
the Caribbean

2014 15

16

17

18

19

20

21

22

23

0

0.2

0.4
IMF AI Preparedness Index

0.6

35

30

25

20

15

10

5

0

P
D
G

f
o
t
i

n
u
r
e
p
s
t
n
e
d

i
c
n

i

I
A
f
o
r
e
b
m
u
N

)
s
r
a
l
l
o
d
S
U

f
o
s
n
o
i
l
l
i
r
t

r
e
p
r
e
b
m
u
N

(

0.8

Sources: AI, Algorithmic, and Automation Incidents and Controversies (AIAAIC); University of Maryland Center for International and Security
Studies; and IMF staﬀ calculations.

Note: AIAAC defines an “incident” as a sudden known or unknown event (or “trigger”) that becomes public and which takes the form of a
disruption, loss, emergency, or crisis. In panel 2, the IMF AI Preparedness Index incorporates four macro-structural indicators that are
relevant for AI adoption: digital infrastructure, innovation and economic integration, human capital and labor market policies, and
regulation and ethics. AI = artificial intelligence.

1Chapter 3 of the April 2024 Global Financial Stability Report

explains how the growing instances of cyberattacks post an
acute threat to macrofinancial stability. Data cited comes from
Figure 3.5, panel 4, of the report.

2There are three databases keeping track of so-called AI inci-
dents: (1) the AI Incident Database (https://incidentdatabase.ai/
apps/incidents/), (2) the Organisation for Economic Co-oper-
ation and Development’s AI Incidents Monitor (https://oecd.
ai/en/incidents), and (3) the AI, Algorithmic, and Automation
Incidents and Controversies Repository (www.aiaaic.org).

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GLOBAL FINANCIAL STABILITY REPORT: STEAdYING ThE COuRSE: uNCERTAINTY, ARTIFICIAL INTELLIGENCE, ANd FINANCIAL STABILITY

Box 3.2 (continued)
largely reflect AI usage rates. This illustrates that the
use of AI comes with risks when it behaves in ways
that were not anticipated.

Recent incidents illustrate potential mechanisms
through which an AI-triggered cybersecurity breach
could lead to more significant ramifications. In
a 2024 case, a finance worker at a multinational
firm in Hong Kong SAR was reportedly tricked by
AI-generated deepfake video and audio, allegedly
leading to a $25 million payout to fraudsters.3
This incident shows how generative AI can be used
to exploit human or organizational vulnerabilities
through personalization and social engineering. In

3Chen, Heather, and Kathleen Magramo. 2024. “Finance
Worker Pays Out $25 Million After Video Call with Deepfake
‘Chief Financial Officer.’” CNN, February 4. https://edition.cnn
.com/2024/02/04/asia/deepfake-cfo-scam-hong-kong-intl-hnk
/index.html

the financial sector, bad actors could gain access to
critical systems.

Cyberattacks can disrupt computer systems, with

implications for key financial markets. Although
a 2023 ransomware attack on the Industrial and
Commercial Bank of China is not known to have
been related to AI, it reportedly affected US Trea-
sury market conditions.4

Finally, fake or genuine social media activity can

amplify news and contribute to panic, possibly
through manipulation. Reports suggest that First
Republic Bank was targeted by an online manipula-
tion campaign.5

4Financial Times. 2023. “Ransomware Attack on ICBC

Disrupts Trades in US Treasury Market.” November 10. https://
www.ft.com/content/8dd2446b-c8da-4854-9edc-bf841069ccb8
5Khan, Amil, and Fergus McKenzie-Wilson. “The First ‘Safe’

Bank Brought Down by Disinformation Attacks.” Valent Proj-
ects, January 2024. https://www.valent-projects.com
/news-and-insights/first-republic-bank-brought-down

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Glossary

This glossary provides descriptions and, where possible, definitions of the most important AI-related concepts,

as used in the chapter. It draws from definitions and descriptions used by international standard setting bodies.

Algorithmic trading
(AT)

Artificial intelligence
(AI)27

Deep learning

FinTech28

Foundation models29

Generative AI

Trading in financial instruments whereby an algorithm independently executes
trading decisions.26 Algorithmic trading can be used for trade execution,
market-making, or in other proprietary trading strategies. Algorithmic trading
strategies vary in complexity and latency; in its simplest guise, algorithmic
trading may involve the use of basic trading rules or instruction to feed portions
of an order into the market at preset intervals to minimize market impact cost.
More complex applications can involve multi-asset trading strategies based on
advanced machine learning models. Reinforcement learning allows algorithms to
learn dynamically from evolving trading patterns, as well as the actions of other
algorithms.

The theory and development of computer systems able to perform tasks that
traditionally have required human intelligence. The definition of AI is very
broad and would include many simple applications that would generally not be
described as AI in the public discourse. For example, simple linear regression
would fall under this broad definition, even though most would not classify this
as AI. The focus of this chapter is on more sophisticated AI, which includes not
only generative AI but also more complex nongenerative applications such as
clustering algorithms, neural networks, gradient-boosted decision trees, support
vector machines, etc.

A form of machine learning that uses algorithms that work in “layers” inspired
by the structure and function of the brain. Deep learning algorithms, whose
structures are called artificial neural networks, can be used for supervised,
unsupervised, or reinforcement learning (itself a form of machine learning).

Technology-enabled innovation in financial services that could result in new
business models, applications, processes, or products with an associated material
effect on the provision of financial services.

An umbrella term referring to a diversity of models that are usually trained
by applying deep learning to massive quantities of data, such as text and
images. Because the expertise, time, and computing power involved in training
foundation models from scratch are typically prohibitive for most nonspecialist
firms, these models are usually pretrained and shared with end users for further
refinement.

AI that generates new content, such as text, images, and videos, often based on
user prompts. Generative AI is powered by foundation models, such as large
language models.

26Financial Conduct Authority Handbook (2011).
27FSB (2017b).
28FSB (2017b).
29FSB (forthcoming).

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High-frequency trading
(HFT)

HFT is frequently equated to algorithmic trading. However, whereas HFT is a type
of algorithmic trading, not all forms of algorithmic trading can be described as
high frequency. Algorithmic trading predates HFT and has been extensively used
as a tool to determine some or all aspects of trade execution like timing, price,
quantity, and venue. Many intermediaries use algorithmic trading for their own
proprietary trading or offer it to their clients. It has also become a standard feature
in many buy-side firms, mainly with the purpose of devising execution strategies
that minimize price impact or to rebalance large portfolios of securities as market
conditions change. A number of common features and trading characteristics related
to HFT are identified by IOSCO.30

Large language models
(LLMs)

Large language models are AI systems designed to learn grammar, syntax, and
semantics of one or more languages to generate coherent and context-relevant
language.

Machine learning (ML)

A method of designing a sequence of actions to solve a problem that optimizes
automatically through experience and with limited or no human intervention.

Market-making31

Proprietary trading

RegTech32

Reinforcement learning
(RL)

Robo-advisors or
automated advice

SupTech

Synthetic data

The provision of liquidity for clients in financial instruments, whereby a trader
sets firm bid-offer quotes and thereby provides liquidity for a specific product or a
particular product class. This is designed to avoid temporary imbalances between
supply and demand for certain products.

Describes a trading unit which is separate from the rest of an organization’s trading
activities and is not involved in client business. It generates profits exclusively from
taking positions. This trading unit has no client contact and is not involved in the
broker market.

Any range of applications of FinTech for regulatory and compliance requirements
and reporting by regulated financial institutions. This can also refer to firms that
offer such applications.

A type of machine learning paradigm where an agent learns to make decisions
by taking actions in an environment to achieve some goal. The learning process
is driven by the feedback the agent receives from the environment in the form of
rewards or penalties.

Applications that combine digital interfaces and algorithms, and can also
include machine learning, to provide services ranging from automated financial
recommendations to contract brokering to portfolio management to their clients,
without or with very limited human intervention. Such advisors may be standalone
firms and platforms or can be in-house applications of incumbent financial
institutions.

Any application of FinTech used by regulatory, supervisory, and oversight
authorities.

Artificially generated information that is designed to mimic real-world data in terms
of statistical properties and structure. Unlike real data, which are directly collected
from real-world events or interactions, synthetic data are created through algorithms
and simulation models.

30IOSCO (2011, p. 22).
31CGFS (2014).
32FSB (2017a).

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References

Adrian, Tobias, Michael J. Fleming, and Erik Vogt. 2017.
“The Evolution of Treasury Market Liquidity: Evidence
from 30 Years of Limit Order Book Data.” Federal Reserve
Bank of New York Staff Reports 827, New York, November.
https://www.newyorkfed.org/research/staff_reports/sr827
Authority for Consumers and Markets. 2024. “Algorithmic

Trading in Wholesale Energy Markets: Key Findings of an
Exploratory Market Study.” July. https://www.acm.nl/system/
files/documents/rapport-marktstudie-algoritmische-handel-
energiemarkt-pl.pdf

Authority for the Financial Markets. 2023. “Machine Learning
in Algorithmic Trading Application by Dutch Proprietary
Trading Firms and Possible Risks.” Amsterdam.

Bank for International Settlements (BIS). 2023. “Principles
for Sound Management of Operational Risk (PSMOR)—
Executive Summary.” Basel, April 27. https://www.bis.org/fsi/
fsisummaries/psmor.pdf

Bank of England. 2024. “DSIT-HMY Letter.” London, April 22.
https://www.bankofengland.co.uk/-/media/boe/files/prudential-
regulation/letter/2024/dsit-hmt-letter.pdf

Baron, Matthew, Jonathan Brogaard, Björn Hagströmer,
and Andrei A. Kirilenko. 2017. “Risk and Return in
High-Frequency Trading.” Journal of Financial and Quantita-
tive Analysis. http://dx.doi.org/10.2139/ssrn.2433118

Basel Committee on Banking Supervision (BCBS). 2013. “Prin-
ciples for Effective Risk Data Aggregation and Risk Report-
ing.” Basel, January. https://www.bis.org/publ/bcbs239.pdf
Basel Committee on Banking Supervision (BCBS). 2021. “Prin-
ciples for Operational Resilience.” Basel. https://www.bis.org/
bcbs/publ/d516.htm

Basel Committee on Banking Supervision (BCBS). 2024. “Dig-
italisation of Finance.” Basel, May. https://www.bis.org/bcbs/
publ/d575.pdf

Basel Committee on Banking Supervision (BCBS), Committee
on Payments and Market Infrastructures (CPMI), and Board
of the International Organization of Securities Commissions
(IOSCO). 2022. “Review of Margining.” Basel. https://www.
bis.org/bcbs/publ/d537.pdf

Boehmer, Ekkehart, Kingsley Fong, and Juan Wu. 2021.

“Algorithmic Trading and Market Quality: International
Evidence.” Journal of Financial and Quantitative Analysis 56
(8): 2659–88. https://www.cambridge.org/core/journals/
journal-of-financial-and-quantitative-analysis/article/abs/
algorithmic-trading-and-market-quality-international-
evidence/4B96E916E3E13AFF1DF9B5FCC188F4E0

Bollerslev, Tim, Viktor Todorov, and Sophia Zhengzi Li. 2013.
“Jumps Tails, Extreme Dependencies and the Distribution of
Stock Returns.” Journal of Econometrics 172: 307–24.

Bonelli, Maxime, and Thierry Foucault. 2023. “Displaced by

Big Data: Evidence from Active Fund Managers.” HEC Paris
Research Paper FIN-2023-1492. https://papers.ssrn.com/sol3/
papers.cfm?abstract_id=4527672

Boukherouaa, El Bachir, Ghiath Shabsigh, Khaled AlAjmi, Jose
Deodoro, Aquiles Farias, Ebru S. Iskender, Alin T. Mirestean,
and others. 2021. “Powering the Digital Economy: Oppor-
tunities and Risks of Artificial Intelligence in Finance.” IMF
Departmental Paper 2021/024, International Monetary Fund,
Washington, DC. https://www.imf.org/-/media/Files/
Publications/DP/2021/English/PDEORAIFEA.ashx

Bouveret, Antoine, Peter Breuer, Yingyuan Chen, David Jones,
and Tsuyoshi Sasaki. 2015. “Fragilities in the U.S. Treasury
Market: Lessons from the ‘Flash Rally’ of October 15, 2014.”
IMF Working Paper 2015/222, International Monetary
Fund, Washington, DC.

Cambridge SupTech Lab. 2023. “State of SupTech Report
2023.” University of Cambridge, Cambridge, UK.
https://www.cambridgesuptechlab.org/SOS

Chaboud, Alain, Benjamin Chiquoine, Erik Hjalmarsson, and
Clara Vega. 2014. “Rise of the Machines: Algorithmic Trad-
ing in the Foreign Exchange Market.” The Journal of Finance
69 (5): 2045–85. https://doi.org/10.1111/jofi.12186.
Chen, Rui, and Jinjuan Ren. 2022. “Do AI-Powered Mutual

Funds Perform Better?” Finance Research Letters 47 (Part A):
102616.

Chen, Yifei, Bryan T. Kelly, and Dacheng Xiu. 2023. “Expected

Returns and Large Language Models.” Unpublished.
doi:10.2139/ssrn.4416687.

Committee on the Global Financial System (CGFS). 2014. “Mar-
ket-Making and Proprietary Trading: Industry Trends, Drivers
and Policy Implications.” CGFS Papers 52, Bank for Interna-
tional Settlements, Basel. https://www.bis.org/publ/cgfs52.pdf
di Castri, Simone, Stefan Hohl, Arend Kulenkampff, and Jermy
Prenio. 2019. “The Suptech Generations.” FSI Insights on
Policy Implementation 19, Financial Stability Institute, Bank
for International Settlements, Basel. https://www.bis.org/fsi/
publ/insights19.pdf

Dou, Winston Wei, Itay Goldstein, and Yan Ji. 2024.

“AI-Powered Trading, Algorithmic Collusion, and Price
Efficiency.” Jacobs Levy Equity Management Center for
Quantitative Financial Research Paper, The Wharton School
Research Paper, Philadelphia, PA. https://papers.ssrn.com/
sol3/papers.cfm?abstract_id=4452704

Epoch AI. 2024. “Training Compute of Frontier AI Mod-
els Grows by 4-5x per Year.” https://epochai.org/blog/
training-compute-of-frontier-ai-models-grows-by-4-5x-per-year

European Securities and Markets Authority. n.d. “Digital

Operational Resilience Act (DORA).” https://www.esma.
europa.eu/esmas-activities/digital-finance-and-innovation/
digital-operational-resilience-act-dora

Fan, Yang, Markus Pelger, and Xintong Yu. Forthcoming. “Do
AI-Algorithmic Traders Lead to Market Instability? A Multi-
agent Reinforcement Learning Approach.” Working paper.
Federal Reserve System, Federal Deposit Insurance Corpora-
tion, and Department of the Treasury. 2023. “Interagency
Guidance on Third-Party Relationships: Risk Management.”

International Monetary Fund | October 2024

103

GLOBAL FINANCIAL STABILITY REPORT: STEAdYING ThE COuRSE: uNCERTAINTY, ARTIFICIAL INTELLIGENCE, ANd FINANCIAL STABILITY

June 6. https://www.occ.gov/news-issuances/news
-releases/2023/nr-ia-2023-53a.pdf

Financial Conduct Authority Handbook. 2011. “Algorithmic
Trading.” https://www.handbook.fca.org.uk/handbook/
glossary/G3552a.html

Financial Stability Board (FSB). 2017a. “Financial Stability
Implications from FinTech: Supervisory and Regulatory
Issues that Merit Authorities’ Attention.” Basel, June 27.
https://www.fsb.org/wp-content/uploads/R270617.pdf
Financial Stability Board (FSB). 2017b. “Artificial Intelli-

gence and Machine Learning in Financial Services: Market
Developments and Financial Stability Implications.” Basel,
November 1. https://www.fsb.org/wp-content/uploads/
P011117.pdf

Financial Stability Board (FSB). 2020. “Effective Practices

for Cyber Incident Response and Recovery Final Report.”
Basel, October 19. https://www.fsb.org/wp-content/uploads/
P191020-1.pdf

Financial Stability Board (FSB). 2023a. “Recommendations to
Achieve Greater Convergence in Cyber Incident Reporting
Final Report.” Basel, April 13. https://www.fsb.org/wp-content/
uploads/P130423-1.pdf

Financial Stability Board (FSB). 2023b. “Enhancing Third-Party
Risk Management and Oversight: A Toolkit for Financial
Institutions and Financial Authorities.” Basel, December 4.
https://www.fsb.org/wp-content/uploads/P041223-1.pdf
Financial Stability Board (FSB). Forthcoming. “Financial Stabil-

ity Implications of Artificial Intelligence.” Basel.

Financial Stability Board (FSB). n.d. “Financial Innovation
and Structural Change.” Basel. https://www.fsb.org/work-
of-the-fsb/financial -innovation-and-structural-change/
non-bank-financial-intermediation/

Gensler, Gary, and Lily Bailey. 2020. “Deep Learning and Finan-
cial Stability.” 1 November 2020. SSRN: https://papers.ssrn.
com/sol3/papers.cfm?abstract_id=3723132

Hendershott, Terrence, Charles M. Jones, and Albert J.

Menkveld. 2011. “Does Algorithmic Trading Improve
Liquidity?” The Journal of Finance 66: 1–33.

Hendershott, Terrence, and Ryan Riordan. 2012. “Algorithmic
Trading and the Market for Liquidity.” Journal of Financial
and Quantitative Analysis 48 (4): 1001–24.

Hong Kong Monetary Authority. 2019. “High-Level Principles

on Artificial Intelligence.” Hong Kong, November 1.

Institute for Workplace Equality. 2022. “The Artificial Intelligence
Technical Advisory Committee Report.” December 16. https://
www.theinstitute4workplaceequality.org/ai-tac-report-release
Institute of International Finance (IIF) and Ernst & Young.
2023. “Annual Survey Report on AI/ML Use in Financial
Services.” Washington, DC.

International Monetary Fund (IMF) and World Bank. 2018.
“The Bali Fintech Agenda.” Washington, DC. http://www.
imf.org/en/Publications/Policy-Papers/Issues/2018/10/11/
pp101118-bali-fintech-agenda

104

International Monetary Fund | October 2024

International Organization of Securities Commissions (IOSCO).
2011. “Regulatory Issues Raised by the Impact of Techno-
logical Changes on Market Integrity and Efficiency. Final
Report.” FR09/111, Madrid. https://www.iosco.org/library/
pubdocs/pdf/IOSCOPD361.pdf

International Organization of Securities Commissions (IOSCO).
2015. “Mechanisms for Trading Venues to Effectively Manage
Electronic Trading Risks and Plans for Business Continuity.”
IOSCO Final Report, Madrid, December. https://www.iosco.
org/library/pubdocs/pdf/IOSCOPD522.pdf

International Organization of Securities Commissions (IOSCO).

2018. “Mechanisms Used by Trading Venues to Manage
Extreme Volatility and Preserve Orderly Trading. Final
Report.” FR 13/2018, Madrid. https://www.iosco.org/library/
pubdocs/pdf/IOSCOPD607.pdf

International Organization of Securities Commissions (IOSCO).

2021. “The Use of Artificial Intelligence and Machine
Learning by Market Intermediaries and Asset Managers. Final
Report.” FR6/2021, Madrid. https://www.iosco.org/library/
pubdocs/pdf/IOSCOPD684.pdf

International Organization of Securities Commissions (IOSCO).
2024. “Update to IOSCO 2023–24 Work Programme—
March 2024–March 2025 Workplan.” OR01/24, Madrid.
https://www.iosco.org/library/pubdocs/pdf/IOSCOPD764
.pdf

Lerner, Josh, Amit Seru, Nicholas Short, and Yuan Sun.
2024. “Financial Innovation in the Twenty-First Cen-
tury: Evidence from US Patents.” Journal of Political
Economy 132 (5). https://www.journals.uchicago.edu/
doi/10.1086/727712

London Stock Exchange Group. 2024. “New Opportunities

Abound for AI in Fixed Income in 2024.” June 3.
Mercer Investments. 2024. “AI Integration in Investment
Management: 2024 Global Manager Survey.” https://
www.mercer.com/insights/investments/portfolio-strategies/
ai-in-investment-management-survey/

Monetary Authority of Singapore. 2018. “Principles to Promote
Fairness, Ethics, Accountability and Transparency in the Use
of Artificial Intelligence and Data Analytics in Singapore’s
Financial Sector.” FEAT Principles, November 12.

Monetary Authority of Singapore. 2024. “Project MindForge.”
May 27. https://www.mas.gov.sg/schemes-and-initiatives/
project-mindforge

National Cyber Security Centre. 2023. “Guidelines for Secure AI
System Development.” November 27. https://www.ncsc.gov.
uk/collection/guidelines-secure-ai-system-development
National Institute of Standards and Technology. 2024. “AI
Risk Management Framework.” https://www.nist.gov/itl/
ai-risk-management-framework

Office of the Superintendent of Financial Institutions. 2023. “A
Canadian Perspective on Responsible AI. Financial Industry
Forum on Artificial Intelligence.” April. https://www.osfi-bsif.
gc.ca/sites/default/files/documents/ai-ia_en.pdf

CHAPTER 3  AdvANCES IN ARTIFICIAL INTELLIGENCE: IMPLICATIONS FOR CAPITAL MARkET ACTIvITIES

Park, Peter S., Simon Goldstein, Aidan O’Gara, Michael Chen,
and Dan Hendrycks. 2023. “AI Deception: A Survey of
Examples, Risks, and Potential Solutions.” Patterns 5 (5):
100988.

Scholtus, Martin, Dick van Dijk, and Bart Frijns. 2014. “Speed,
Algorithmic Trading, and Market Quality Around Macro-
economic News Announcements.” Journal of Banking and
Finance 38 (1): 89–105. doi:10.1016/j.jbankfin.2013.09.016

Securities and Exchange Board of India. 2019. “Reporting for
Artificial Intelligence (AI) and Machine Learning (ML)
Applications and Systems Offered and Used by Mutual
Funds.” Mumbai, May 9. https://www.sebi.gov.in/legal/
circulars/may-2019/reporting-for-artificial-intelligence-ai-and-
machine-learning-ml-applications-and-systems-offered-and-
used-by-mutual-funds_42932.html

Shabsigh, Ghiath, and El Bachir Boukherouaa. 2023. “Genera-
tive Artificial Intelligence in Finance: Risk Considerations.”
IMF Fintech Notes 2023/006, International Monetary Fund,
Washington, DC. https://www.imf.org/en/Publications
/fintech-notes/Issues/2023/08/18/Generative-Artificial
-Intelligence-in-Finance-Risk-Considerations-537570

Swinkels, Lauren, and Tobias Hoogteijling. June 2022.

“Forecasting Stock Crash Risk with Machine Learning.”
https://www.robeco.com/files/docm/docu-202206-forecasting-
stock-crash-risk-with-machine-learning-hksg.pdf

US National Archives 2021
US Securities and Exchange Commission. 2020. “Staff Report on

Algorithmic Trading in U.S. Capital Markets.” Washington, DC.

Vaswani, Ashish, Noam M. Shazeer, Niki Parmar, Jakob

Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and
others. 2017. “Attention Is All You Need.” Neural Informa-
tion Processing Systems.

Vereckey, Betsy. 2023. “The Dark Side of Stock Market

Circuit Breakers.” MIT Management Sloan School, Feb-
ruary 16. https://mitsloan.mit.edu/ideas-made-to-matter/
dark-side-stock-market-circuit-breakers

Weller, Brian. 2017. “Does Algorithmic Trading Deter Informa-

tion Acquisition?” https://ssrn.com/abstract=2662254 or http://
dx.doi.org/10.2139/ssrn.2662254

Yadav, Yesha. 2015. “How Algorithmic Trading Undermines

Efficiency in Capital Markets.” Vanderbilt Law Review 68 (6):
1607. https://scholarship.law.vanderbilt.edu/vlr/vol68/iss6/3/

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Timely.Topical.Free.Global economics at your fingertipsIMF.org/pubs | bookstore.IMF.org | eLibrary.IMF.orgIN THIS ISSUE:

CHAPTER 1
Steadying the Course:
Financial Markets
Navigate Uncertainty

CHAPTER 2
Macrofinancial Stability Amid
High Economic Uncertainty

CHAPTER 3
Advances in Artificial Intelligence:
Implications for Capital
Market Activities

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GLOBAL FINANCIAL STABILITY REPORT  OCTOBER 2024

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