A class of distorted Gaussian copulas: theories and applications
提出一种新的扭曲高斯连接函数（dGAB copula），能更好地捕捉尾部相关性，并展示了其在篮子违约互换定价中的应用，同时给出了基于EM算法的参数估计方法。
This study introduces a novel copula class, referred to as the distorted GAB copula (hereafter, dGAB copula), as an alternative to the Gaussian copula, which has shown limitations in capturing tail dependence. Much like the Gaussian copula, the dGAB copula can be uniquely determined by its bivariate marginal copulas and offers effective tail dependence modeling capabilities. To demonstrate its practical applicability, we showcase its use in the valuation of basket default swaps. Furthermore, we propose a parameter estimation approach based on the EM algorithm tailored to the dGAB copula.