Monte Carlo Algorithms for Default Timing Problems

Management Science, Vol. 57, No. 12, pp. 2115-2129, 2011

31 Pages Posted: 16 Sep 2010 Last revised: 18 Mar 2012

See all articles by Kay Giesecke

Kay Giesecke

Stanford University - Management Science & Engineering

Baeho Kim

Korea University Business School (KUBS)

Shilin Zhu

Stanford University - Department of Statistics

Date Written: March 14, 2011

Abstract

Dynamic, intensity based point process models are widely used to measure and price the correlated default risk in portfolios of credit-sensitive assets such as loans and corporate bonds. Monte Carlo simulation is an important tool to perform computations in these models. This paper develops, analyzes and evaluates two simulation algorithms for these models. The algorithms extend the conventional thinning scheme to the case where the event intensity is unbounded, a feature common to many standard model formulations. Numerical results illustrate the performance of the algorithms for a familiar top-down model and a novel bottom-up model of correlated default risk.

Suggested Citation

Giesecke, Kay and Kim, Baeho and Zhu, Shilin, Monte Carlo Algorithms for Default Timing Problems (March 14, 2011). Management Science, Vol. 57, No. 12, pp. 2115-2129, 2011. Available at SSRN: https://ssrn.com/abstract=1677150 or http://dx.doi.org/10.2139/ssrn.1677150

Kay Giesecke (Contact Author)

Stanford University - Management Science & Engineering ( email )

473 Via Ortega
Stanford, CA 94305-9025
United States
(650) 723 9265 (Phone)
(650) 723 1614 (Fax)

HOME PAGE: http://www.stanford.edu/~giesecke/

Baeho Kim

Korea University Business School (KUBS) ( email )

Anam-dong, Sungbuk-Gu
Korea University Business School
Seoul, 136-701
82-2-3290-2626 (Phone)
82-2-922-7220 (Fax)

HOME PAGE: http://biz.korea.ac.kr/~baehokim

Shilin Zhu

Stanford University - Department of Statistics ( email )

Stanford, CA 94305
United States

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