Sequential Importance Sampling and Resampling for Dynamic Portfolio Credit Risk

Shaojie Deng (), Kay Giesecke () and Tze Leung Lai ()
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Shaojie Deng: Microsoft, Redmond, Washington 98052
Kay Giesecke: Department of Management Science and Engineering, Stanford University, Stanford, California 94305
Tze Leung Lai: Department of Statistics, Stanford University, Stanford, California 94305

Operations Research, 2012, vol. 60, issue 1, 78-91

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.

Keywords: event timing models; portfolio credit risk; rare-event simulation (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (5)

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