Modelling And Management Of Credit Risk Of Chinese Commercial Banks

Since the reform and opening in 1978, China’s banking industry has made considerable progress. But there is still a gap between Chinese financial markets and banking sector and those of developed countries, with underdeveloped capital markets, single level of the financial markets. In Eighth Session of Third Plenary in 2014, Shang Fulin indicated that, China’s indirect financing ratio is more than 80% and banking assets accounted for more than 90% of total financial assets. In the context of economic and financial globalization, the domestic economy is facing the dual pressures of insufficient external demand and sluggish domestic demand, and structural imbalance pressure highlights, with excess capacity, a lot of business is facing tremendous pressure, credit risk control pressure of commercial banks rises. Therefore, the study of commercial bank credit risk measurement and management is important.This paper discusses commercial bank credit risk measurement method and selects KMV model to measure credit risk of China’s commercial banks. In the theoretical model, the paper introduces an improved Merton model, namely the principle of Merton-KMV model. Then Merton-KMV model is extended to applicable to the capital structure with two classes of debt, with this theoretical model derived in detail.In the empirical research, this paper picks out the data from 2012 to 2014 of 9 ST companies, that are special treated in 2015, and 9 non ST companies from Shanghai & Shenzhen stock market as sample, use Merton-KMV model to model the companies’ credit risk, computes theoretical EDF, and comparatively analyzes the results. At the same time, the paper uses Z-score model to calculate the sample’s Z value for three years. The empirical results show that, from 2012 to 2014, Z values of ST and non-ST companies have no significant differences, and show no trend changes over time. However, the results of KMV model show that the default probability of non ST companies is significantly lower than that of ST, and the significance increased over time. Therefore, static financial data are insufficient to fully identify credit risk, only financial data and market data together include the company’s credit information. using the Merton-KMV method for analysis of such information to calculate the theoretical EDF can better identify credit risk of two types of corporate, and is forward-looking.Therefore, in the case of insufficient credit history data of listed companies, commercial banks can use KMV model to estimate the theoretical EDF of listed companies to measure credit risk.