中国金融机构风险关联性: 基于 DBN-LGCNET 多层网络的计量研究

The structure of the financial system is constantly changing under the impact of the macro environment, and risk spillover is the key to analyze systemic risk. In order to break through the dimension limitation and model specification of traditional parametric models, this paper proposes a semiparametric method, Dynamic Bayesian-Local Gaussian Correlation Network (DBN-LGCNET) to measure the time-varying nonlinear correlation between the general and tail risks. The model is applied to the data of 65 listed financial institutions in China’s A-share market, and the results show that: 1) There are obvious tail risk spillovers in the financial system. 2) Risk spillover in the financial industry display heterogeneity, with the source of general risk propagation mainly in the banking sector and the source of tail risk propagation mainly in the securities sector. 3) Risks propagate dynamically among financial institutions, state-owned banks demonstrate a consistent capacity to absorb risk spillovers, whereas small and medium-sized banks show a lesser ability to cope with extreme events. 4) After an extreme event, the impact of the banking industry in the general correlation network is enhanced and the impact of the securities industry is weakened. Links between financial institutions in the tail correlation network are strengthened, especially insurance institutions.