Research Of How To Apply Improved KMV Model In Chinese Commercial Banks

On May 24,2019,Baoshang Bank was announced to be supervised by People's bank of China and China Banking and Insurance Regulatory Commission for one year due to its credit risk problems,which sounded the alarm for other commercial banks.This also shows that the traditional credit risk assessment methods of China's banking industry are insufficient,and the credit risk of China's commercial banks should be further studied.In practical application,with the deepening of research,KMV(Kealhofer,M-cquown and Vasicek)model has been gradually highlighted by its scientific,ob-jective,operational and practical advantages.So it has been trusted and adopted by more and more researchers.In this paper,we selected the relevant data of the existing commercial banks in China,used the KMV model to evaluate,and improved the KMV model ac-cording to the evaluation results to make it more suitable for our country banking industry:equity value calculated by equity structure,stock yields calculated by GARCH(1,1)model,and default point calculated by value of total liabilities se-lected with MATLAB.The PFM(Private Firm Model)Model developed based on KMV Model,and it is used to evaluate the unlisted commercial banks.Based on these two models,some commercial banks in China are selected as examples to calculate the default distance(DD)and the expected default rate(EDF).After empirical analysis,we compared the credit rating of banks with that of relevant credit rating organizations,and relevant conclusions were drawn.In this paper,it is found that listed bank's greater default distance means farther distance from the default point,which in turns means a smaller default probability expected by the theory,and an obvious inverse relationship does ex-ist.When the listed bank's risk-free rate becomes smaller,the asset value also decreases,and the volatility of asset value increases.It shows that the reduc-tion of the risk-free interest rate in China reduces some credit risk of the listed commercial banks.The results obtained by listed banks using KMV model is basically consistent with the latest bank subject rating of 2019 on sohu finance and economics,which indicates that KMV model has reference value for risk measurement of listed banks.In unlisted banks,the reverse relationship between default distance and probability of default theory still holds.However,the results obtained by unlisted banks using PFM model is not consistent with the latest bank subject rating of 2019 on sohu finance and economics,which indicates that the PFM model has some limitations on the risk measurement of unlisted banks.But if we only see the results alone,the default distance data of listed Banks and unlisted Banks conforms to the basic normal state,which indicates that this study has certain reference value.According to the comprehensive analysis found that with the continuous improvement of unlisted bank's data and model's main parameters,the study of these two models will also promote the overall credit risk measurement of China's banking industry.At last,some suggestions are given for the problems in the banking industry,such as the difficulty in obtain-ing data and the high non-performing loan ratio.The prospect of more suitable credit risk measurement models in the development of the banking industry is also proposed.