Systemic Risk Contribution and Contagion of Industrial Sectors in China: From the Global Financial Crisis to the COVID-19 Pandemic

Abstract

This paper investigates the risk contribution of 29 industrial sectors to the China stock market by using one-factor with Durante generator copulas (FDG) and component expected shortfall (CES) analyses. Risk contagion between the systemically most important sector and other sectors is examined using a copula-based ∆CoVaR approach. The data cover the 2008 global financial crisis and the beginning of the COVID-19 pandemic. The empirical results show that the banking sector contributed most to systemic risk before and during the global financial crisis. Nonbank finance became equally important in 2020, and the COVID-19 pandemic promoted the position of the computer and pharmaceuticals sectors. The spillover effect diminishes over time, but there remains risk contagion between sectors. The risk spillover trend is consistent with that of systemic risk.

Comparison of Systemic Financial Risks in the US before and after the COVID-19 Outbreak—A Copula–GARCH with CES Approach

“…There are some flaws in the study as well. With the development of copulas, vine copulas and factor copulas have been employed to fit high dimensional data [8,38]. Because this study has over 60 variables, we may use factor copulas to fit the data.…”

“…The outbreak of the COVID-19 epidemic has severely shaken global financial markets. Obviously, this epidemic has had a dramatic impact on the stock markets, futures markets, and bond markets of various countries for more than two years, since the beginning of 2020 [7][8][9][10][11][12]. In particular, the US financial market has been greatly impacted, thus increasing the risk of the global financial market [13,14].…”

“…Dependence analysis is important for effective risk management and portfolio management [8]. Many studies [3,32,34,35] used dynamic conditional correlation-generalized autoregressive conditional heteroskedasticity (DCC-GARCH) models to estimate linear correlations of financial assets and further measure financial risk.…”

“…Many studies [3,32,34,35] used dynamic conditional correlation-generalized autoregressive conditional heteroskedasticity (DCC-GARCH) models to estimate linear correlations of financial assets and further measure financial risk. However, many scholars have verified that nonlinear correlation and tail correlations are more favored between financial assets [8,36,37]. Because linear correlation cannot capture the tail correlation and asymmetric dependence of financial assets, the DCC-GARCH approach may underestimate risk [38][39][40].…”