Quantitative Risk Management in Volatile Markets with an Expectile-Based Framework for the FTSE Index
Abiodun Finbarrs Oketunji
2025-07-21
Summary
This paper talks about a new method for managing financial risks in volatile markets, especially for the FTSE 100 index, using expectile-based techniques instead of traditional methods.
What's the problem?
The problem is that traditional risk measures like Value-at-Risk (VaR) often fail during times of market stress, like during financial crises, because they don’t respond well to extreme losses or rapidly changing conditions.
What's the solution?
The authors developed an expectile-based framework that is more sensitive to the size of extreme losses and better adapts to changing market volatility. They used advanced mathematical models and tested them on 20 years of market data, showing that their approach predicts risk more accurately and remains stable during crises.
Why it matters?
This matters because better risk prediction helps financial institutions avoid big losses during volatile times, improves regulatory compliance, and provides investors with more reliable tools to manage their portfolios safely.
Abstract
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.