Monte Carlo Algorithms for Default Timing Problems

Authors:
Kay Giesecke;Baeho Kim;Shilin Zhu
Affiliations:
Department of Management Science and Engineering, Stanford University, Stanford, California 94305;Korea University Business School, Anam-dong, Sungbuk-gu, Seoul 136-701, Korea;Department of Statistics, Stanford University, Stanford, California 94305
Venue:
Management Science
Year:
2011

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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 for performing computations in these models. This paper develops, analyzes, and evaluates two simulation algorithms for intensity-based point process 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. This paper was accepted by Assaf Zeevi, stochastic models and simulation.