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Multiscale Intensity Models and Name Grouping for Valuation of Multi-Name Credit Derivatives

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  • Evan Papageorgiou
  • Ronnie Sircar

Abstract

The pricing of collateralized debt obligations (CDOs) and other basket credit derivatives is contingent upon (i) a realistic modelling of the firms' default times and the correlation between them, and (ii) efficient computational methods for computing the portfolio loss distribution from the individual firms' default time distributions. Factor models, a widely used class of pricing models, are computationally tractable despite the large dimension of the pricing problem, thus satisfying issue (ii), but to have any hope of calibrating CDO data, numerically intense versions of these models are required. We revisit the intensity-based modelling setup for basket credit derivatives and, with the aforementioned issues in mind, we propose improvements (a) via incorporating fast mean-reverting stochastic volatility in the default intensity processes, and (b) by considering homogeneous groups within the original set of firms. This can be thought of as a hybrid of top-down and bottom-up approaches. We present a calibration example, including data in the midst of the 2008 financial credit crisis, and discuss the relative performance of the framework.

Suggested Citation

  • Evan Papageorgiou & Ronnie Sircar, 2009. "Multiscale Intensity Models and Name Grouping for Valuation of Multi-Name Credit Derivatives," Applied Mathematical Finance, Taylor & Francis Journals, vol. 16(4), pages 353-383.
    • Handle: RePEc:taf:apmtfi:v:16:y:2009:i:4:p:353-383
    DOI: 10.1080/13504860902765545
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    Cited by:

    1. Herbertsson, Alexander, 2022. "Saddlepoint approximations for credit portfolios with stochastic recoveries," Working Papers in Economics 823, University of Gothenburg, Department of Economics.
    2. Kazuki Nagashima & Tsz-Kin Chung & Keiichi Tanaka, 2014. "Asymptotic Expansion Formula of Option Price Under Multifactor Heston Model," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 21(4), pages 351-396, November.

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