Description:
The COVID-19 pandemic has had a severe negative effect on the global financial markets and economy. This study investigates how two outbreaks of the pandemic in China affected Chinese stock market industries. Conditional autoregressive value at risk (CAViaR) model is applied to calculate the tail risks for 16 different industries, and risk correlation networks are constructed to describe the risk correlations among industries during various times 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 outbreaks of COVID-19 have resulted in tail risks for most industries in the Chinese stock market. (ii) The risk correlation network became more compact as a result of both outbreaks. And the impact of the second outbreak on the network was less severe than that of the first outbreak. (iii) During the first outbreak of COVID-19, the Financials industry was the primary source of risk output, while during the second outbreak, due to the concentrated outbreak in Shanghai, industries closely related to the city's economy and trade became the most significant risk industries. These findings have practical implications for researchers and decision makers regarding the risk contagion among stock market industries under major public emergencies