An Importance Sampling Method for Portfolio CVaR Estimation with Gaussian Copula Models

We developed an importance sampling method to estimate Conditional Value-at-Risk for portfolios in which interdependent asset losses are modeled via a Gaussian copula model. Our method constructs an importance sampling distribution by shifting the latent variables of the Gaussian copula and thus can handle arbitrary marginal asset distributions. It admits an intuitive geometric explanation and is easy to implement. We also present numerical experiments that confirm its superior performance compared to the naive approach.