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Particle Methods For The Estimation Of Credit Portfolio Loss Distributions

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Author Info

  • RENÉ CARMONA

    ()
    (Bendheim Center for Finance, ORFE, Princeton University, Princeton, NJ 08544, USA)

  • STÉPHANE CRÉPEY

    (Département de Mathématiques, Université d'Évry Val d'Essonne, 91025 Évry Cedex, France)

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    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.

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    Bibliographic Info

    Article provided by World Scientific Publishing Co. Pte. Ltd. in its journal International Journal of Theoretical and Applied Finance.

    Volume (Year): 13 (2010)
    Issue (Month): 04 ()
    Pages: 577-602

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    Handle: RePEc:wsi:ijtafx:v:13:y:2010:i:04:p:577-602

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    Related research

    Keywords: Importance sampling; interacting particle systems; rare events; credit portfolios; loss distribution estimation;

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    Cited by:
    1. Morio, Jérôme & Jacquemart, Damien & Balesdent, Mathieu & Marzat, Julien, 2013. "Optimisation of interacting particle systems for rare event estimation," Computational Statistics & Data Analysis, Elsevier, vol. 66(C), pages 117-128.

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