Sequential Importance Sampling and Resampling for Dynamic Portfolio Credit Risk

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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Year of publication:	2011
Authors:	Deng, Shaojie
Other Persons:	Giesecke, Kay (contributor) ; Lai, Tze (contributor)
Publisher:	[2011]: [S.l.] : SSRN
Subject:	Kreditrisiko \| Credit risk \| Portfolio-Management \| Portfolio selection \| Stichprobenerhebung \| Sampling \| Theorie \| Theory \| Monte-Carlo-Simulation \| Monte Carlo simulation

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