Measuring And Analyzing Credit Risk Of China Electronic Business Company

With the development of Internet technology and popularity of networks, Net Economy cause a strong impact on the traditional economy. Electronic Commerce develop at an alarming rate in recent years, gradually penetrated into every part of economy and social life, now has become hot topics in social-economic development. As the product of Network economy era, the rapid de-velopment of electronic commerce provides a new opportunity and platform for business, but also affects the development of other industries. But in the eco-nomic market, Electronic Commerce Industry develops rapidly, the credit risk is following closely, the impact of the credit default risk in the financial system is enormous. So to strengthen credit risk control and find duly and effectively quantitative methods to measure the credit risk is particularly important.Firstly, this paper describes in detail principle and content of KMV credit risk measurement model. We consider listed companies in the electronic com-merce industry, the sample is the financial data and market data about three listed companies, we provide quantitative analysis results of the e-commerce company’s credit risk from08to13years, and demonstrate its evolution. Fur-ther according to the model we calculate the main risk indicators and asset volatility index, this paper analyses the practical results. This paper provides credit risk measurement about e-commerce listed companies from theoretical and practical results.Secondly,this article consider the complexity of financial data and the dynamic nature of market risk, we introduce the latest dynamic risk measure theory G-Expectation. The principle and measurement method of KMV model driven by G-Brownian motion will be described in detail, and then on the basis of actual market data in2008-2013, we calculate the trajectory of credit risk and main risk indicators about ecommerce listed companies.Finally, paper contracts the practical results (such as default distance, the probability of default and expected loss), then analyzes the differences of the two credit risk measurement models. We hoped the results of the quantitative analysis based on various models will provide the appropriate theoretical and empirical support. This result can help monitoring and management of the e-commerce company’s credit risk.