Network Risk Analysis Of Systemically Important Financial Institutions

The interdependence and dynamics among financial institutions are often tackled in the study of systemic risk.In this article,we combine the“systemic linkage”and the network analysis to address interdependence among the U.S.stock market and Chinese stock market,respectively.More specifically,we quantify dynamic systemic linkages among Ameirican and Chinese SIFIs(Systemically Important Financial Institutions)through time-varying adjacency matrices related to an MEVT(Multivariate Extreme Value Theory)approach using daily closing price data and visualize them using net-work analysis.It is discovered that systemic linkages were enhancive obviously under extreme conditions especially when large negative shocks happened in the financial sys-tem.Meanwhile,we use a TENQR(Tail Event driven Network Quantile Regression)model to address the interdependence and dynamics of the network.Estimation results exhibit that the network factors repond more strongly when the market is under a stress.