关键词: 系统风险, 银行网络, 贝叶斯方法, 同业借贷, 风险管理

Abstract: Interbank exposure is one of the main channels of risk contagion especially in terms of systemic risks.However, the theoretical models in this field have standardized and generalized specifications and don't capture banks' characteristics in reality. In this sense, this study is of practical significance as it is based onthe balance sheet data of real players in the China's financial system. Due to the lack of bilateral exposures between banks, interbank liability matrices must be estimated. The most widely used estimation method, such as entropy maximization, gives point estimator that underestimates systemic risks, while a large number of interbank liability matrices are sampled with the application of Bayesian methodology.The Gibbs samplers of three different network specifications are constructed. Moreover, most of the previous literature only has listed banksor a dozen of largest banks as samples, ignoring more than one hundred smaller banks which are also indispensable for the whole system.
The aim of this study is to give a better understanding of the systemic risk in China's interbank market and to identify the susceptible or robust institutions. Based on the balance sheet data of 185 banks in China, including the "Big Six"; 12 joint-equity banks; 98 city banks; 42 rural banks; and 27 foreign banks, the systemic risk arising from interbank liabilities is examined by the Bayesian methodology which samples a large number of interbank liability matrices.Then the default probabilities of individual banks are generated after negative loss shocks. To the best of our knowledge, it might be the first paper in the Chinese context to measure systemic risks based on the simulation of interbank liability networks. The results show that the structure of interbank liability matrices can remarkably impact on the default probabilities of financial institutions. The scope of being influenced is wider if the network connection probability is in the intermediate level. In a complete network, the effect of risk sharing exceeds that of risk contagion. To conclude, the interbank exposure not only enables the institutions to share risks but also provides the channel for spreading risks. This role transformation depends on the interaction of the following factors:the characteristics of shocks, e.g. the shock size, the number of attacked banks as well as the category of attacked banks; the recovery rate of liquidation; the structure of banks' balance sheets.

Key words: systemic risk, interbank network, Bayesian methodology, interbank loan, risk management