Analysis of tail risk contagion among industry sectors in the Chinese stock market during the COVID-19 pandemic

Abstract

The COVID-19 pandemic has inflicted substantial impacts on global financial markets and the economy. This study investigates the impact of two pandemic outbreaks in China on industry sectors in its stock market. The Conditional Autoregressive Value at Risk (CAViaR) model is applied to compute tail risks across 16 selected industry sectors. In Addition, risk correlation networks are constructed to illustrate the risk correlations among industry sectors during different stages of the two outbreaks. Furthermore, risk contagion networks are built based on the Granger causality test to examine the similarities and differences in the contagion mechanisms between the two outbreaks. The findings of this study show that (i) the two pandemic outbreaks have resulted in tail risks across most industries in the Chinese stock market. (ii) The risk correlation network exhibited increased compactness during both outbreaks, with the impact of the second outbreak on the network being less severe than that of the first outbreak. (iii) During the first outbreak, the financial industry was the primary source of risk output. In contrast, during the second outbreak, the localized nature of the outbreak in Shanghai resulted in the industries closely linked to the city's economy and trade becoming the most significant risk contributors. These empirical findings have practical implications for both researchers and decision-makers, offering insight into the dynamics of risk contagion among stock market industries during major public emergencies.