Application of network vector autoregression model in volatility spillover analysis

Measuring the network connectedness of the financial system is of great importance in systemic risk analysis, and has drawn great attention in recent years. In this paper, we apply the transfer entropy method to analyze the volatility spillover network connectedness of the U.S. stock market. Based on the network structure, we apply the network vector autoregression model (NVAM) and are interested in identifying the influential firms in volatility spillover network of the financial system. In addition, by using rolling windows, the dynamics of total volatility spillover network connectedness indices are obtained, which shows a sharp rise at the beginning of the financial crisis, while it only fluctuates within a controllable range in the steady economic period. The results show that transfer entropy has great potential for understanding the correlation and information flow of financial markets.