Pricing Basket Credit Default Swap Based on Mix Copula Functions

Abstract

This paper deals with modeling dependence structure of credit risks. The choice of tail dependency structure is important on the pricing of multi-name credit derivatives such as basket credit default swap. An alternative to the Gaussian copula is mix Copula consisting of three types of Archimedean Copulas (Gumbel Copula, Clayton Copula and Frank Copula), which capture fat tail dependence structure between the underlying variables at extreme values. By using Monte Carlo simulation, we find that the tail dependence of mix Copula functions is better than that of normal Gaussian Copula functions. Based on the characteristic of copulas, this paper builds up the pricing model of Basket Credit Default Swap, and creates the pricing framework.