Sequential Importance Sampling and Resampling for Dynamic Portfolio Credit Risk

被引：7

作者：

Deng, Shaojie ^{[1
]}

Giesecke, Kay ^{[2
]}

Lai, Tze Leung ^{[3
]}

机构：

[1] Microsoft Corp, Redmond, WA 98052 USA

[2] Stanford Univ, Dept Management Sci & Engn, Stanford, CA 94305 USA

[3] Stanford Univ, Dept Stat, Stanford, CA 94305 USA

来源：

OPERATIONS RESEARCH | 2012年 / 60卷 / 01期

基金：

美国国家科学基金会;

关键词：

SIMULATION;

D O I：

10.1287/opre.1110.1008

中图分类号：

C93 [管理学];

学科分类号：

12 ; 1201 ; 1202 ; 120202 ;

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.

引用

页码：78 / 91

页数：14

共 23 条

[1]

[Anonymous], 2006, Journal of Derivatives

[2] PARTICLE METHODS FOR THE ESTIMATION OF CREDIT PORTFOLIO LOSS DISTRIBUTIONS [J].