Research On Credit Risk Of Green Credit Of Commercial Bank Based On KMV-BP

Over the past 40 years of carrying out the reform and opening policy,the cost of China's rapid economic development is the deterioration of the environment,with the increasingly prominence of environmental problems,China regards sustainable development as its economic development strategy,in the financial field,green finance has become a trend,as a major component of green finance,green credit has gradually played a positive role in the protection of the ecological environment.For commercial banks and other financial institutions,with the continuous development of green credit business,they are more and more likely to face the threat of green credit risk.The development of green credit in China is relatively late,at present,it lags behind the developed countries in credit risk identification and control,which results in the limitation of green credit business development in China.Paper industry listed companies not only have the drawbacks of environmental pollution,but also are closely related to forest resources and have the characteristics of sustainability,which conform to the types of enterprises supported by green credit policy.Therefore,this paper selects 21 paper industry listed companies as the sample companies of empirical research on green credit,and explores effective ways for commercial banks to manage green credit risk.Firstly,the paper introduces the basic situation of green credit,including its connotation,current situation at home and abroad,and calculation of green credit risk in detail;then,it provides a new method for credit risk identification of green credit through empirical research,the specific process is to divide the sample companies into two categories according to whether they have been ST or considering the length of suspension,and select KMV model and BP neural network as the measurement model.The credit risk of sample companies is studied by KMV model,according to the company's financial data to calculate the default distance,this paper finds that duration of suspension is negatively correlated with the default distance;then,based on the calculation results of KMV model,the green credit risk of sample companies is studied by BP neural network,this paper selects the default distance,sustainable development level,moral hazard and media attention as the influencing factors,default distance and suspension time represent financial default risk of enterprises,sustainable development level,moral hazard and media attention represent environmental-related green risk of enterprises,the results show that sustainable development level and media attention are negatively correlated with green credit risk,and moral hazard is positively related to green credit risk;finally,the results show that sustainable development level and media attention are negatively correlated with green credit risk,and moral hazard is positively related to green credit risk;finally,based on the empirical results and the current situation of green credit development in china,and this paper puts forward feasible suggestions for green credit risk prevention and management of commercial bank from two aspects of external environment construction and internal business development of banks.