Research On The Credit Risk Measurement Of Commercial Banks In China

Credit risk is the main risk for commercial banks. Accurate measurement of the creditrisk is not only the inherent requirement of the operation of commercial banks, but also thereality needs of dealing with the New Basel Capital Accord. From the current reality of ourcountry, the credit risk measurement is the weak link of commercial bank credit riskmanagement, which restricts the implementation of the IRB and the healthy developmentof commercial banks. In order to strengthen the credit risk management of commercialBanks in China, this paper takes commercial bank credit risk as the main object of study,takes construct the credit risk evaluation model as the core, and on the basis of learningand learn from the domestic and foreign advanced credit risk measurement techniques,using domestic sample data to build different credit risk measurement model, so as toprovide technical support for China's commercial banks credit risk management.This paper starting from commercial banks credit risk characteristics and causes,according to the latest international and domestic credit risk regulatory requirements,introduced internal ratings and external ratings of credit risk, and according to the reality ofour country, it is recommended that the two should be joint development. On the basis ofcomparison and analysis of credit risk measurement methods and models, this paper usingthe Z-score model as an example, empirically examined the effects of direct application ofthe foreign mature model in China, confirmed that the applicable model must be based ondomestic conditions. After selected, the method of multivariate discriminant analysis andLogistic regression analysis that based on accounting data and market value was chosen forthe main method of build commercial banks credit risk measurement models in ourcountry.In order to establish the model, the article details the financial factors of commercialbanks credit risk measurement, analyzed the selection approach and dimension reduction techniques of the credit risk factors, established the idea that using stepwise selectionfiltering variables and using principal component analysis for data dimension reduction.Then, according to the definition of financial distress and domestic habits, selected78financial normal company samples and78financial distress company samples, and a totalof33financial ratios of the seven categories from our stock market, using2010data andSPSS software to build a six-variable stepwise discriminant analysis model and afour-variable Logit model. Taking into account the financial data with high dimension andhigh correlation, using the method of principal component analysis extracted5principalcomponents from18indicators which have significant group differences, and constructed adiscriminant analysis model and a Logit model which based on principal componentanalysis.Upon examination, four models are effective models, and have certain predictivepower in advance. By comparison, the accuracy of the Logit model is higher than thediscriminant analysis model, but the stability of the discriminant analysis model is betterthan the Logit model. Also found that the principal component analysis can improve thepredictive power in advance and the stability of the model, and that the Logit model ismore significant. In addition, the four models have the feature of follow macroeconomicfluctuations in the same direction change. We called it "Procyclicality". During aneconomic boom, each model is loose, and vice versa. By appropriate adjustments to thecritical point, you can change the ratio of the two types of error rates and thus weaken the"Procyclicality".The results of the study show that multivariate discriminant analysis and Logisticregression analysis are still effective credit risk measurement methods. The principalcomponent analysis is effective data dimensionality reduction method. The models thatbuilt in this paper can be used to measure the credit risk of commercial banks in China.