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Dynamic Conditioning and Credit Correlation Baskets

Author

Listed:
  • Albanese, Claudio
  • Vidler, Alicia

Abstract

Dynamic conditioning is a technique that allows one to formulate correlation models for large baskets without incurring in the curse of dimensionality. The individual price processes for each reference name can be described by a lattice model specified semi-parametrically or even nonparametrically and which can realistically have about 1000 sites. The time discretization step is chosen so small to satisfy the Courant stability condition and is typically of about a few hours. This constraint ensures needed smoothness for the single name probability kernels which can thus be directly manipulated. A flexible multi-factor correlation model can be obtained by means of conditioning trees corresponding to binomial processes with jumps. There is one conditioning tree associated to each reference names, one associated to each industry sector and a global one to the basket itself. Since the conditioning trees are correlated, the underlying processes are also mutually correlated. In this paper, we discuss a modeling framework for CDOs based on dynamic conditioning in greater detail than previously done in our other papers. We also show that the model calibrates well to index tranches throughout in the period from 2005 to the Spring of 2008 and yields instructive insights.

Suggested Citation

  • Albanese, Claudio & Vidler, Alicia, 2008. "Dynamic Conditioning and Credit Correlation Baskets," MPRA Paper 8368, University Library of Munich, Germany, revised 21 Apr 2008.
  • Handle: RePEc:pra:mprapa:8368
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    File URL: https://mpra.ub.uni-muenchen.de/8368/1/MPRA_paper_8368.pdf
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    References listed on IDEAS

    as
    1. Albanese, Claudio & Vidler, Alicia, 2007. "A STRUCTURAL MODEL FOR CREDIT-EQUITY DERIVATIVES AND BESPOKE CDOs," MPRA Paper 5227, University Library of Munich, Germany, revised 09 Sep 2007.
    2. Lucas, Andre & Klaassen, Pieter & Spreij, Peter & Straetmans, Stefan, 2001. "An analytic approach to credit risk of large corporate bond and loan portfolios," Journal of Banking & Finance, Elsevier, vol. 25(9), pages 1635-1664, September.
    3. Claudio Albanese & Adel Osseiran, 2007. "Moment Methods for Exotic Volatility Derivatives," Papers 0710.2991, arXiv.org.
    4. Albanese, Claudio, 2006. "Operator Methods, Abelian Processes And Dynamic Conditioning," MPRA Paper 5246, University Library of Munich, Germany, revised 06 Nov 2007.
    5. Albanese, Claudio & Chen, Oliver X., 2006. "Implied migration rates from credit barrier models," Journal of Banking & Finance, Elsevier, vol. 30(2), pages 607-626, February.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Gunter Meissner & Seth Rooder & Kristofor Fan, 2013. "The impact of different correlation approaches on valuing credit default swaps with counterparty risk," Quantitative Finance, Taylor & Francis Journals, vol. 13(12), pages 1903-1913, December.

    More about this item

    Keywords

    CDO; pricing; dynamic conditioning; correlation modeling; semi-parametric; operator methods;

    JEL classification:

    • E50 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - General
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

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