Efficient importance sampling for reduced form models in credit risk

Achal Bassamboo*, Sachin Jain

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

In this paper we study the problem of estimating probability of large losses in the framework of doubly stochastic credit risk models. We derive a logarithmic asymptote for the probability of interest in a specific asymptotic regime and propose an asymptotically optimal importance sampling algorithm for efficiently estimating the same. The numerical results in the last section corroborate our theoretical findings.

Original languageEnglish (US)
Title of host publicationProceedings of the 2006 Winter Simulation Conference, WSC
Pages741-748
Number of pages8
DOIs
StatePublished - Dec 1 2006
Event2006 Winter Simulation Conference, WSC - Monterey, CA, United States
Duration: Dec 3 2006Dec 6 2006

Publication series

NameProceedings - Winter Simulation Conference
ISSN (Print)0891-7736

Other

Other2006 Winter Simulation Conference, WSC
CountryUnited States
CityMonterey, CA
Period12/3/0612/6/06

ASJC Scopus subject areas

  • Software
  • Modeling and Simulation
  • Computer Science Applications

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  • Cite this

    Bassamboo, A., & Jain, S. (2006). Efficient importance sampling for reduced form models in credit risk. In Proceedings of the 2006 Winter Simulation Conference, WSC (pp. 741-748). [4117678] (Proceedings - Winter Simulation Conference). https://doi.org/10.1109/WSC.2006.323154