The Research Of Credit Risk Management Practical Application In Our Country' Commercial Bank

Credit risk is one of the most important risks which banks are confronted with. Especial - lly with the development of the global economic integration and liberalization of financial reform, Credit risk problem becoming more severe.The objective of credit risk management is to hope that through bank timely discovery the repayment ability of borrowers to control the risk ability, thus reducing the likelihood of the risk loss for commercial Banks . At present, In order to improve the ability of credit risk management,the major international financial institutions have developed various types of credit risk models. This article is intended to improve the existing model,the primary contents are as follows:Chapter one introduces the background and significance of the research., research status of domestic and foreign and the ideas and structure of this study.Chapter two introduces the definition of credit risk, basic characteristics, the causes of Credit risk in our country'commercial banks and commonly used method of risk quantification.Chapter three is one of the two main contents of this paper- introduce the Logistic model based on principal component analysis in the credit risk management. First we select related financial indicators separately from the company profitability, operation capacity, capacity de -velopment and short and long term debt paying ability, Then using principal component anal -ysis build a listed company comprehensive evaluation index, Finally, using the Logistic mod -el on the listed company's credit risk measurement.Chapter four is another main content in this paper. It introduces the basic principles of KMV model. Because time series data have the peaks ,heavy tails and heteroscedasticity characteristics, We adopt students - t distribution and generalized error distribution to describe the fat tail characteristics,And using heteroskedasticity model GARCH model, TGARCH model and the EGARCH model to process data heteroscedasticity. Then create a suitable model to estimate the volatility of equity, and finally calculate the default distance to measure credit risk.We test the default distance from different model,The results show that the model could distinguish ST firms and normal companies.Chapter five is the summary of this paper.