The Generalized Extreme Value (GEV) distribution plays a critical role in risk assessment across various domains, such as hydrology, climate science, and finance. In this study, we investigate its application in analyzing intraday trading risks within the Chinese stock market, focusing on abrupt price movements influenced by unique trading regulations. To address limitations of traditional GEV parameter estimators, we leverage recently developed robust and asymptotically normal estimators, enabling accurate modeling of extreme intraday price fluctuations. We introduce two risk indicators: the mean risk level (mEVI) and a Stability Indicator (STI) to evaluate the stability of the shape parameter over time. Using data from 261 Chinese and 32 U.S. stocks (2015-2017), we find that Chinese stocks exhibit higher mEVI, corresponding to greater tail risk, while maintaining high model stability. Additionally, we show that Value at Risk (VaR) estimates derived from our GEV models outperform traditional GP and normal-based VaR methods in terms of variance and portfolio optimization. These findings underscore the versatility and efficiency of GEV modeling for intraday risk management and portfolio strategies.

翻译：广义极值（GEV）分布在洪水、气候科学及金融等领域的风险评估中发挥着关键作用。本研究探讨了其在中国股市日内交易风险分析中的应用，重点关注受独特交易规则影响的股价突变行为。针对传统GEV参数估计量的局限性，我们采用近期开发的具有稳健性与渐近正态性的估计量，从而实现对极端日内价格波动的精确建模。我们引入两个风险指标：平均风险水平（mEVI）与稳定性指标（STI），用于评估形状参数随时间变化的稳定性。基于261只中国股票与32只美国股票（2015-2017年）的数据分析发现，中国股票展现出更高的mEVI（对应更厚的尾部风险），同时保持较高的模型稳定性。此外，研究显示基于GEV模型计算的风险价值（VaR）估计量，在方差与投资组合优化方面均优于传统的广义帕累托（GP）分布与正态分布VaR方法。这些发现印证了GEV模型在日内风险管理与投资组合策略制定中的普适性与高效性。