Particle methods for the estimation of credit portfolio loss distributions

Rene A. Carmona, Stéphane Crépey

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

The goal of the paper is the numerical analysis of the performance of Monte Carlo simulation based methods for the computation of credit-portfolio loss-distributions in the context of Markovian intensity models of credit risk. We concentrate on two of the most frequently touted methods of variance reduction in the case of stochastic processes: importance sampling (IS) and interacting particle systems (IPS) based algorithms. Because the subtle differences between these methods are often misunderstood, as IPS is often regarded as a mere particular case of IP, we describe in detail the two kinds of algorithms, and we highlight their fundamental differences. We then proceed to a detailed comparative case study based on benchmark numerical experiments chosen for their popularity in the quantitative finance circles.

Original languageEnglish (US)
Pages (from-to)577-602
Number of pages26
JournalInternational Journal of Theoretical and Applied Finance
Volume13
Issue number4
DOIs
StatePublished - Jun 2010

All Science Journal Classification (ASJC) codes

  • Finance
  • Economics, Econometrics and Finance(all)

Keywords

  • Importance sampling
  • credit portfolios, loss distribution estimation
  • interacting particle systems
  • rare events

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