地方法人银行违约风险及其对系统性风险影响的研究
A Study on the Default Risk of Local Incorporate Banks and the Impact on Systemic Risk
史永东 1姜尚 2邢伟泽3
作者信息
- 1. 东北财经大学金融科技学院、金融学院
- 2. 东北财经大学金融学院
- 3. 东北财经大学金融科技学院
- 折叠
摘要
近年来,地方法人银行违约风险持续增加,银行体系承压明显,若发生风险事件,不仅会冲击金融市场,而且在金融市场内传播还会引发系统性风险.本文采用复杂网络方法,通过模拟系统性风险的形成过程,甄别我国35家银行对系统性风险的影响发现:2016年以来,对系统性风险贡献较大的银行多为地方法人银行;地方法人银行系统重要性明显低于国有商业银行和股份制银行,但违约概率最高.此外,本文还发现,使用机器学习方法能够较好地识别和预测银行违约行为,银行内外部特征和其违约行为存在明显的非线性关联.
Abstract
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.
关键词
地方法人银行/违约风险/系统性风险/复杂网络/机器学习Key words
Local Incorporate Banks/Default Risks/Systemic Risks/Complex Network/Machine Learning引用本文复制引用
出版年
2024
NETL
NSTL
万方数据