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Monte Carlo Algorithms for Default Timing Problems

Author

Listed:
  • 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)

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.

Suggested Citation

  • Kay Giesecke & Baeho Kim & Shilin Zhu, 2011. "Monte Carlo Algorithms for Default Timing Problems," Management Science, INFORMS, vol. 57(12), pages 2115-2129, December.
    • Handle: RePEc:inm:ormnsc:v:57:y:2011:i:12:p:2115-2129
    DOI: 10.1287/mnsc.1110.1411
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    References listed on IDEAS

    as
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    Cited by:

    1. Dianfa Chen & Jun Deng & Jianfen Feng & Bin Zou, 2017. "An Explicit Default Contagion Model and Its Application to Credit Derivatives Pricing," Papers 1706.06285, arXiv.org, revised Aug 2018.
    2. Dassios, Angelos & Zhao, Hongbiao, 2017. "A generalised contagion process with an application to credit risk," LSE Research Online Documents on Economics 68558, London School of Economics and Political Science, LSE Library.
    3. Xiaowei Zhang & Jose Blanchet & Kay Giesecke & Peter W. Glynn, 2015. "Affine Point Processes: Approximation and Efficient Simulation," Mathematics of Operations Research, INFORMS, vol. 40(4), pages 797-819, October.
    4. Angelos Dassios & Hongbiao Zhao, 2017. "A Generalized Contagion Process With An Application To Credit Risk," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 20(01), pages 1-33, February.
    5. Dassios, Angelos & Zhao, Hongbiao, 2017. "Efficient simulation of clustering jumps with CIR intensity," LSE Research Online Documents on Economics 74205, London School of Economics and Political Science, LSE Library.

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