The Empirical Study On The Credit Risk Measurement Of Listed Real Estate Companies

In the area of finance, the accurate measurement and effective management for credit risk have been one of the most challenging topics. The subprime mortgage crisis in2007is warning the whole world of the necessity and significance of credit risk measurement and management once more, and has made the credit risk measurement technology and management method attract more and more attention. The subprime mortgage crisis is essentially the credit crisis, which is related to the real estate market bubble. It is the collapse of the bubble that directly caused America’s subprime crisis, and eventually evolved into the whole financial system’s crisis. Therefore, how to effectively control and manage the credit risk, particularly related to the real estate industry’s credit risk has already aroused great attention from financial regulators, financial institutions and investors. In the context of this reality, on the basis of in-depth research of moden credit risk management models, and combing with the actual situation of Chinese real estate companies, we aims to explore the accuracy and validity of this models, which has very important practical significance for China’s banks and financial market.First of all, this thesis introduces the definition and characteristics of the credit risk, discusses the meaning of the credit risk management and analyses the important fundament role of the credit risk measurement in the credit risk management; Next, we has a detailed reviews and comments on modern credit risk management models, and considers that the KMV model is superior to other credit risk evaluation models. Then based on the Black and Scholes (1973) and Merton (1974)(BSM)’s contingent claims model, and combing with KMV Corporation’s framework, we estimate the distance to default for a sample of99listed real estate companies over the period2002to2009. It shows that, KMV model is valid in measuring the credit risk of the real estate companies in China. The sensitivity analysis shows that the volatility of stock value is the most sensitive to the distance to default, but the change of the default point doesn’t have significant effect on the distance to default. We also found, the credit risk level of listed real estate companies in China fluctuate wildly from2002to2009, and the credit risk is larger. According to the DD frequency, we set DD=2and2.5as the early warning line. Finally, we explore the Logistic model’s performance in credit risk measurement, then put Logistic model and KMVmodel into a comparative analyse.The study shows that:Logistic model is able to estimate and predict listed real estate companies’ credit risk in an effective way. KMVmodel and Logistic model have a great consistency in listed real estate companies’ credit risk assessment.