Sequential Importance Sampling and Resampling for Dynamic Portfolio Credit Risk

• 成果类型: Article
• 署名作者: Deng, Shaojie; Giesecke, Kay; Lai, Tze Leung
• 署名单位: Microsoft; Stanford University; Stanford University
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.1110.1008
• 发表日期: 2012
• 页码: 78-91
• 关键词: simulation

摘要

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.