Research On Credit Risk Correlation Between Industries:Based On Copula Function

Credit risk is one of the most important financial risks, and has always been concerned by the financial sector. The increasing openness of financial markets continues to enhance close economic ties between economic entities, thus the credit risk represents new features, namely credit risk correlation. Often an economic entity credit default will spread to other economic entities in a short period of time, leading to the deterioration or even bankruptcy of the credit status of other economic entities. This kind of infection and spread among economic entities make the small range of credit risk lead to the instability of the overall economic environment, triggering a severe financial crisis. In the context of the continuous infection incidents of credit risk, only to consider the individual economic agents of credit risk could not adapt to changes in complex financial environment, therefore, research on credit risk correlation plays an increasingly important role in credit risk management.This paper discusses the measurement and correlation of the industry credit risk to build an industry credit risk correlation framework based on systematic analysis of the credit risk and the correlation theory, as well as KMV model and Copula function. Specific tasks of this paper are as follow: First, KMV model is corrected. The default distance of KMV model on the recognition ability of the listing corporation credit risk has been proved from the perspective of a single listed company, which provides a micro foundation for the selection of macro industry credit risk measurement; Second, taking wholesale and retail industry as an example, on the basis of the full analysis of the Copula function correlation characteristics, this paper builds a M-Copula function to fit the related structures of default distance between two industries and analyzes the relationship between two industry credit risk.The results show that different Copula correlations focus on different descriptions of correlation characterization, and single Copula function can only describe one side of the related structures of default distance between two industries; M-Copula function can describe the relationship of default distance well in the wholesale and retail industry and can capture the changes of their correlation features flexibly. By studying the related structure of the default distance on wholesale and retail industry, it finds that there is an asymmetric relationship between two industries, and a strong correlation in the lower tail, which means that when the credit risk of an industry becomes larger, the possibility of another industry credit risk also increases greatly, and changes in the credit risk of the two joint industries significantly is enhanced too. When choosing the credit portfolio, commercial banks will consider the correlation industry credit risk, by evading overconcentrating in these two industries or similar thereto to avoid a sharp deterioration of credit risk while credit portfolio is subjected to negative shocks caused by a strong tail correlation between two industries.