Efficient Algorithms for Basket Default Swap Pricing with Multivariate Archimedean Copulas

We introduce a new importance sampling method for pricing basket default swaps based on exchangeable Archimedean copulas and nested Gumbel copulas. We establish more realistic dependence structure than the existing copula models for credit risks in the underlying portfolio, and propose an appropriate density for importance sampling by analyzing multivariate Archimedean copulas. To justify efficiency and accuracy of our proposed algorithms, we demonstrate several numerical examples compared with the crude Monte Carlo simulation, and show that our proposed estimators produce remarkably small variance with accurately expected values in pricing basket default swaps