模型不确定下我国商业银行系统性风险影响因素分析
发布时间：2018-08-29 08:22
【摘要】：本文采用成分期望损失CES方法,基于公开市场数据,对我国16家上市商业银行的系统性风险进行度量。基于CES的方法无论从时间维度还是横截面维度上,都与我国银行业的实际情况有着较好的契合。本文还采用贝叶斯模型平均BMA方法,广泛纳入现有相关文献中选取的影响因子作为解释变量,解决传统回归中的模型不确定性。研究结果表明,对于我国上市商业银行而言,银行规模、股权市账比及是否处于系统重要性地位与银行系统性风险呈现出显著的正相关关系,而非利息收入的提高能够有效地分散系统性风险;在行业层面,银行系统的波动率越高,单个机构面临的系统性风险也越大。以上结论可以为银行监管部门政策制定提供较为明确的启示及实证支持。
[Abstract]:Based on the open market data, this paper measures the systemic risk of 16 listed commercial banks in China by using the component expectation loss (CES) method. The method based on CES has a good agreement with the actual situation of China's banking industry in terms of both time dimension and cross section dimension. In this paper, the Bayesian model average BMA method is also used to solve the uncertainty of the traditional regression model by incorporating the influence factors selected in the existing literature as explanatory variables. The results show that, for the listed commercial banks in China, there is a significant positive correlation between the size of the banks, the equity market / book ratio and whether they are systemically important to the systemic risk of the banks. The increase in non-interest income can effectively disperse systemic risk; at the industry level, the higher the volatility of the banking system, the greater the systemic risk faced by individual institutions. The above conclusions can provide clear enlightenment and empirical support for bank regulatory policy formulation.
【作者单位】： 武汉大学经济与管理学院;
【基金】：国家社科基金重大项目(15ZDC020) 国家自然科学基金面上项目(71673205) 武汉大学自主科研项目(人文社会科学)的阶段性研究成果
【分类号】：F832.33
，
本文编号：2210729
[Abstract]:Based on the open market data, this paper measures the systemic risk of 16 listed commercial banks in China by using the component expectation loss (CES) method. The method based on CES has a good agreement with the actual situation of China's banking industry in terms of both time dimension and cross section dimension. In this paper, the Bayesian model average BMA method is also used to solve the uncertainty of the traditional regression model by incorporating the influence factors selected in the existing literature as explanatory variables. The results show that, for the listed commercial banks in China, there is a significant positive correlation between the size of the banks, the equity market / book ratio and whether they are systemically important to the systemic risk of the banks. The increase in non-interest income can effectively disperse systemic risk; at the industry level, the higher the volatility of the banking system, the greater the systemic risk faced by individual institutions. The above conclusions can provide clear enlightenment and empirical support for bank regulatory policy formulation.
【作者单位】： 武汉大学经济与管理学院;
【基金】：国家社科基金重大项目(15ZDC020) 国家自然科学基金面上项目(71673205) 武汉大学自主科研项目(人文社会科学)的阶段性研究成果
【分类号】：F832.33
，
本文编号：2210729
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