The connectedness, structure and performance of different financial networks

We conduct a comparative analysis of quantitative models for assessing risk contagion and systemic risk within the Chinese financial market, focusing on four key methodologies: vector autoregression-forecast error variance decomposition (VAR-FEVD), quantile vector autoregression-forecast error variance decomposition QVAR-FEVD, linear conditional value-at-risk (CoVaR) and tail-event driven network (TENET). Our analysis underscores the significance of network construction methods in accurately depicting the spillover effects among financial institutions. The research delves into the performance of financial networks by comparing “physical†networks, evaluating predefined networks within the dynamic network quantile regression model, and identifying systemically important financial institutions across various network configurations. In particular, the TENET model emerges as particularly adept, outperforming the other models in capturing both mean and tail risk spillovers. This paper not only deepens the understanding of systemic risk in China but also provides valuable recommendations for policy makers to design effective regulatory frameworks to mitigate potential crises.