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Stochastic Intensity Modeling For Structured Credit Exotics

Author

Listed:
  • ALEXANDER CHAPOVSKY

    (Merrill Lynch International, 2 King Edward Street, London EC1A 1HQ, United Kingdom)

  • ANDREW RENNIE

    (Merrill Lynch International, 2 King Edward Street, London EC1A 1HQ, United Kingdom)

  • PEDRO TAVARES

    (Merrill Lynch International, 2 King Edward Street, London EC1A 1HQ, United Kingdom)

Abstract

We propose a class of credit models where we model default intensity as a jump-diffusion stochastic process. We demonstrate how this class of models can be specialised to value multi-asset derivatives such as CDO and CDO2 in an efficient way. We also suggest how it can be adapted to the pricing of option on tranche and leverage tranche deals. We discuss how the model performs when calibrated to the market.

Suggested Citation

  • Alexander Chapovsky & Andrew Rennie & Pedro Tavares, 2007. "Stochastic Intensity Modeling For Structured Credit Exotics," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 10(04), pages 633-652.
    • Handle: RePEc:wsi:ijtafx:v:10:y:2007:i:04:n:s0219024907004330
    DOI: 10.1142/S0219024907004330
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    References listed on IDEAS

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    1. Longstaff, Francis A & Schwartz, Eduardo S, 2001. "Valuing American Options by Simulation: A Simple Least-Squares Approach," University of California at Los Angeles, Anderson Graduate School of Management qt43n1k4jb, Anderson Graduate School of Management, UCLA.
    2. Longstaff, Francis A & Schwartz, Eduardo S, 2001. "Valuing American Options by Simulation: A Simple Least-Squares Approach," The Review of Financial Studies, Society for Financial Studies, vol. 14(1), pages 113-147.
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    Cited by:

    1. Balakrishna, B S, 2010. "Levy Subordinator Model of Default Dependency," MPRA Paper 21386, University Library of Munich, Germany.
    2. Balakrishna, B S, 2010. "Levy Subordinator Model: A Two Parameter Model of Default Dependency," MPRA Paper 26274, University Library of Munich, Germany.

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