Optimal Importance Sampling of Default Losses

Risk management applications often require estimating the tail distribution of total default losses on a portfolio of credit-sensitive positions such as loans and corporate bonds. This paper develops, analyzes and tests an importance sampling estimator of large-loss probabilities. The estimator is implementable for any reduced-form model of correlated name-by-name default timing. Technical conditions guarantee the asymptotic optimality of the estimator, and numerical results illustrate its performance. The significance of the conditions is studied