A Study on the Default Risk of Local Incorporate Banks and the Impact on Systemic Risk
In recent years,the default risk of local incorporate banks has increased,intensifying pressure on the banking system.It has become a principal method of preventing and resolving systemic risks by screening the weak points of the bank-ing system and disposing risks prospectively.This study employs data from 35 Chinese listed banks to estimate the default probability of the banks using the CCA method.Furthermore,it employs complex network technology to simulate the formation process of systemic risk and identify the systemic risk of 35 banks.The results show that local incorporate banks have contributed significantly to systemic risk since 2016.Although the systemic importance of local incorporate banks is significantly lower than that of state-owned and joint-stock banks,their probability of default is considerably higher than that of the other two types of banks,resulting in a greater contribution to systemic risk.To proactively prevent and mitigate potential risks in the banking system,this paper uses machine learning methods to identify and predict bank default.The results indicate that this method can effectively predict bank default,and most of the internal and external characteristics of banks have significant nonlinear correlations with bank de-faults.This paper contributes to the literature in several ways.Firstly,this paper presents a more systematic analysis of the dy-namic relationship between the default risk of local incorporate banks and systemic risk.Secondly,this paper explores the rea-sons for the significant systemic risk of local incorporate banks by analyzing the formation process of systemic risk.Thirdly,this paper employs a machine learning approach to forecast bank defaults and examine the key factors that precipitate the default of local incorporate bank.
Local Incorporate BanksDefault RisksSystemic RisksComplex NetworkMachine Learning
NETL
NSTL
万方数据
