Monte Carlo Algorithms for Default Timing Problems

Kay Giesecke (), Baeho Kim () and Shilin Zhu ()
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Kay Giesecke: Department of Management Science and Engineering, Stanford University, Stanford, California 94305
Baeho Kim: Korea University Business School, Anam-dong, Sungbuk-gu, Seoul 136-701, Korea
Shilin Zhu: Department of Statistics, Stanford University, Stanford, California 94305

Management Science, 2011, vol. 57, issue 12, 2115-2129

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.

Keywords: simulation; probability; stochastic model applications; financial institutions; banks (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (6)

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