Quantitative Risk Management in Volatile Markets with an Expectile-Based
  Framework for the FTSE Index
This research presents a framework for quantitative risk management in
volatile markets, specifically focusing on expectile-based methodologies
applied to the FTSE 100 index. Traditional risk measures such as Value-at-Risk
(VaR) have demonstrated significant limitations during periods of market
stress, as evidenced during the 2008 financial crisis and subsequent volatile
periods. This study develops an advanced expectile-based framework that
addresses the shortcomings of conventional quantile-based approaches by
providing greater sensitivity to tail losses and improved stability in extreme
market conditions. The research employs a dataset spanning two decades of FTSE
100 returns, incorporating periods of high volatility, market crashes, and
recovery phases. Our methodology introduces novel mathematical formulations for
expectile regression models, enhanced threshold determination techniques using
time series analysis, and robust backtesting procedures. The empirical results
demonstrate that expectile-based Value-at-Risk (EVaR) consistently outperforms
traditional VaR measures across various confidence levels and market
conditions. The framework exhibits superior performance during volatile
periods, with reduced model risk and enhanced predictive accuracy. Furthermore,
the study establishes practical implementation guidelines for financial
institutions and provides evidence-based recommendations for regulatory
compliance and portfolio management. The findings contribute significantly to
the literature on financial risk management and offer practical tools for
practitioners dealing with volatile market environments.
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Finbarrs Oketunji
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Quantitative Risk Management in Volatile Markets with an Expectile-Based
  Framework for the FTSE Index
This research presents a framework for quantitative risk management in
volatile markets, specifically focusing on expectile-based methodologies
applied to the FTSE 100 index. Traditional risk measures such as Value-at-Risk
(VaR) have demonstrated significant limitations during periods of market
stress, as evidenced during the 2008 financial crisis and subsequent volatile
periods. This study develops an advanced expectile-based framework that
addresses the shortcomings of conventional quantile-based approaches by
providing greater sensitivity to tail losses and improved stability in extreme
market conditions. The research employs a dataset spanning two decades of FTSE
100 returns, incorporating periods of high volatility, market crashes, and
recovery phases. Our methodology introduces novel mathematical formulations for
expectile regression models, enhanced threshold determination techniques using
time series analysis, and robust backtesting procedures. The empirical results
demonstrate that expectile-based Value-at-Risk (EVaR) consistently outperforms
traditional VaR measures across various confidence levels and market
conditions. The framework exhibits superior performance during volatile
periods, with reduced model risk and enhanced predictive accuracy. Furthermore,
the study establishes practical implementation guidelines for financial
institutions and provides evidence-based recommendations for regulatory
compliance and portfolio management. The findings contribute significantly to
the literature on financial risk management and offer practical tools for
practitioners dealing with volatile market environments.