Systemic importance analysis of chinese financial institutions based on volatility spillover network

From the perspective of volatility spillover, this paper investigates systemic importance and its influential factors of Chinese financial institutions by complex network modeling method. We first construct the volatility spillover networks by vector autoregressive models-multivariate generalized autoregressive conditional heteroscedastic models (VAR-MGARCH) in a BEKK form, and then construct a comprehensive network centrality index based on five network centralities (degree centrality, closeness centrality, betweenness centrality, modified Katz centrality and information centrality) to measure the financial institutions' systemic importance. The results indicate that the larger comprehensive network centrality index is, the higher corresponding ranking for the node of networks is and the greater systemic importance of financial institution will be. Finally, we identify the major factors which affect systemic importance of the financial institutions with panel data regression analysis. We find that compared with the market factors, the accounting factors are more advantageous to identify important financial institutions. Specifically, financial institutions with lager size and higher assets growth rate tend to be associated with greater systemic importance.