Efficient Importance Sampling for Reduced Form Models in Credit Risk

In this paper we study the problem of estimating probability of large losses in the framework of doubly stochastic credit risk models. We derive a logarithmic asymptote for the probability of interest in a specific asymptotic regime and propose an asymptotically optimal importance sampling algorithm for efficiently estimating the same. The numerical results corroborate our theoretical findings