Empirical Research and Analysis on the Volatility of Chinese Insurance Stocks in the Post-Pandemic Period

Zhaoxuan Luo

doi:10.6981/FEM.202511_6(11).0012

Authors

Zhaoxuan Luo

DOI:

https://doi.org/10.6981/FEM.202511_6(11).0012

Keywords:

Insurance Stocks; Volatility; GARCH Model; COVID-19 Pandemic; Asymmetry.

Abstract

The global outbreak of COVID-19 exerted a substantial influence on worldwide financial systems, resulting in marked transformations in the volatility behavior of insurance sector equities in China. Based on an analysis of daily stock prices from five major A-share insurers, including Ping An and China Life, between November 2019 and January 2023, this study applies three types of models-GARCH, EGARCH, and GJR-GARCH-combined with Student's t-distribution and skewed Student's t-distribution. The results show that returns exhibit notable leptokurtosis, fat tails, right-skewness, and strong volatility persistence and clustering. While the EGARCH model provides the best in-sample fit, GJR-GARCH performs better in out-of-sample forecasts. Negative policy events are found to significantly amplify volatility, with clear cross-company differences in asymmetry. These findings suggest persistent and asymmetric volatility in the post-pandemic era, necessitating more sophisticated modelling approaches. Practical implications include the need for tailored risk management strategies and enhanced dynamic monitoring mechanisms for investors, regulators, and insurance companies.

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