Jump volatility spillover network based measurement of systemic importance of Chinese financial institutions
利用5分钟高频数据构建跳跃波动溢出网络，通过出强度和入强度识别中国系统重要性金融机构，并分析其决定因素。
Abstract The identification of systemically important financial institutions (SIFIs) is an important measure to deal with systemic risks. To achieve this goal, we first use generalized variance decomposition method and granger causality test to construct jump volatility spillover networks of Chinese financial institutions based on the 5‐min high‐frequency data. Then, out‐strength and in‐strength are adopted to analyze the SIFI. Finally, we use panel data regression model to investigate the determinant of the SIFIs. The empirical results show that: (a) The network density reaches a peak when the financial system under pressure during the China's stock market disaster of 2015. (b) Large banks and insurances usually display systemic importance, while some small financial institutions are also SIFIs due to their high value of out‐strength and in‐strength. (c) There are obvious differences in the factors that affect the out‐strength and in‐strength based on panel data regression model, but turnover rate, jump volatility, firm size and growth rate of total assets are the common driving factors.