46. Copula, Correlated Defaults, and Credit VaR

Almost every financial institution devotes a lot of attention and energy to credit risk. The default correlations of credit assets have a fatal influence on credit risk. How to model default correlation correctly has become a prerequisite for the effective management of credit risk. In this thesis, we provide a new approach to estimating future credit risk on target portfolio based on the framework of CreditMetrics^TM by J.P. Morgan. However, we adopt the perspective of factor copula and then bring the principal component analysis concept into factor structure to construct a more appropriate dependence structure among credits. In order to examine the proposed method, we use real market data instead of virtual ones. We also develop a tool for risk analysis that is convenient to use, especially for banking loan businesses. The results indicate that people assume dependence structures are normally distributed, which could lead to underestimated risks. On the other hand, our proposed method captures better features of risks, including conspicuous fat-tail effects, even though the factors appear normally distributed.