Can Generalized Extreme Value Model Fit the Real Stocks

TL;DR

This paper demonstrates that the Generalized Extreme Value (GEV) distribution, combined with robust estimators, effectively models intraday stock risks, outperforming traditional methods in risk estimation and portfolio optimization.

Contribution

It introduces new robust estimators for GEV parameters and applies them to intraday stock risk analysis, highlighting their effectiveness in capturing tail risks and stability over time.

Findings

Chinese stocks have higher tail risk (mEVI) than U.S. stocks.

GEV-based VaR estimates outperform traditional methods.

High model stability observed in Chinese stocks.

Abstract

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.