Sequential Importance Sampling and Resampling for Dynamic Portfolio Credit Risk

by Shaojie Deng of Microsoft,
Kay Giesecke of Stanford University, and
Tze Leung Lai of Stanford University

March 4, 2011

Abstract: We provide a sequential Monte Carlo method for estimating rare-event probabilities in dynamic, intensity-based point process models of portfolio credit risk. The method is based on a change of measure and involves a resampling mechanism. We propose resampling weights that lead, under technical conditions, to a logarithmically efficient simulation estimator of the probability of large portfolio losses. A numerical analysis illustrates the features of the method, and contrasts it with other rare-event schemes recently developed for portfolio credit risk, including an interacting particle scheme and an importance sampling scheme.

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