基於期望值的框架下富時指數於波動市場中的量化風險管理

Quantitative Risk Management in Volatile Markets with an Expectile-Based Framework for the FTSE Index

July 16, 2025

作者: Abiodun Finbarrs Oketunji

cs.AI

摘要

本研究提出了一种针对波动市场中的定量风险管理框架，特别聚焦于应用于富时100指数的期望分位数方法。传统的风险度量指标，如风险价值（VaR），在市场压力时期表现出显著局限性，这在2008年金融危机及随后的波动期间得到了验证。本研究开发了一种先进的基于期望分位数的框架，通过提供对尾部损失的更高敏感度及在极端市场条件下的改进稳定性，解决了传统基于分位数方法的不足。研究采用了一个涵盖二十年富时100指数收益率的数据集，包括高波动性时期、市场崩盘及复苏阶段。我们的方法引入了期望分位数回归模型的新颖数学公式，利用时间序列分析增强的阈值确定技术，以及稳健的回测程序。实证结果表明，基于期望分位数的风险价值（EVaR）在各种置信水平和市场条件下均优于传统的VaR度量。该框架在波动时期表现出卓越性能，降低了模型风险并提高了预测准确性。此外，本研究为金融机构确立了实际实施指南，并为监管合规和投资组合管理提供了基于证据的建议。这些发现对金融风险管理文献做出了重要贡献，并为应对波动市场环境的从业者提供了实用工具。

English

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.