Credit Risk Evaluation Of Listed Commercial Banks

As commercial banks play an important role in promoting national economic development and maintaining financial stability,the smooth operation of the banking industry plays an important role in the healthy development of China's economy.However,due to the unique nature of bank debt management,the development of the banking industry has high risk characteristics.Therefore,the management of commercial bank risks is of great significance to the country's overall macroeconomic and financial control.This paper first introduced some theoretical research on the risks of commercial banks,including the classification of bank risks,the definition of credit risks and the causes.Then introduced several commonly used credit risk measurement methods,including modern credit risk measurement model,statistical credit risk measurement model and artificial intelligence credit risk measurement.Considering all of this,the KMV model is used to evaluate the credit risk of commercial banks.Then,the basic principle and solution process of KMV model are introduced in detail.At the same time,the method for solving the volatility of equity value in KMV model is improved.The traditional variance solution method is abandoned,and the dynamic GARCH model is used for fitting the data.Then is the empirical part of the full text.This paper selects five major state-owned banks and Shanghai Pudong Development Bank,Minsheng Bank,Industrial Bank etc.A total of 13 listed medium and large commercial banks.And 11 listed local banks including Hangzhou Bank,Changshu Bank,Guiyang Bank and Wujiang Bank etc.as research target.Using the stock market data of these sample banks from 2015 to 2017,the modified KMV model was established,and the credit risk of these banks was compared and analyzed by finding the default distance and default probability of the sample banks.It was found that due to the overall fluctuation of the stock market in 2015,the probability of default of the sample banks was relatively large,and the default probability of Ningbo Bank was the highest,reaching 15.07%.In 2017,the default probability of Changshu Bank,Wujiang Bank,Jiangyin Bank and Wuxi Bank was higher than that of other sample banks.Since the probability of default in 2015 is quite different from that in 2016 and 2017,the paired sample T test is conducted for the three-year data.The test results show that the probability of default in 2015 is significantly different from 2016 and 2017.2016 There is no significant difference in the probability of default between the year and 2017.Therefore,it can be concluded that the external impact of the stock market has a great impact on the credit risk of banks.After solving the default probability,the financial indicators of these banks are used to construct the evaluation index system,and the index system is simplified by factor analysis to obtain four common factors.Finally,the default probability and four common factors are obtained to make regression model,through the regression model,the loan quality factor and the fund liquidity level factor passed the significance test.the bank's probability of default has a lot to do with the bank's loan quality and the bank's liquidity level.