Sequential Importance Sampling and Resampling for Dynamic Portfolio Credit Risk

Authors:
Shaojie Deng;Kay Giesecke;Tze Leung Lai
Affiliations:
Microsoft, Redmond, Washington 98052;Department of Management Science and Engineering, Stanford University, Stanford, California 94305;Department of Statistics, Stanford University, Stanford, California 94305
Venue:
Operations Research
Year:
2012

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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.