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Default Intensities implied by CDO Spreads: Inversion Formula and Model Calibration

Rama Cont

Imperial College London; CNRS

Romain Deguest


Yu Hang (Gabriel) Kan

Bloomberg Tradebook; Columbia University - Department of Industrial Engineering and Operations Research (IEOR)

April 2010

We propose a simple computational method for constructing an arbitrage-free CDO pricing model which matches a pre-specified set of CDO tranche spreads. The key ingredient of the method is a formula for computing the local default intensity function of a portfolio from its expected tranche notionals. This formula can be seen as an analog, for portfolio credit derivatives, of the well-known Dupire formula. Together with a quadratic programming method for recovering expected tranche notionals from CDO spreads, our inversion formula leads to an efficient non-parametric method for calibrating CDO pricing models.

Comparing this approach to other calibration methods, we find that model-dependent quantities such as the forward starting tranche spreads and jump-to-default ratios are quite sensitive to the calibration method used, even within the same model class. On the other hand, comparing the local default intensities implied by different credit portfolio models reveals that apparently very different models such as static Student-t copula models and reduced-form affine jump-diffusion models, lead to similar marginal loss distributions and tranche spreads.

Number of Pages in PDF File: 39

Keywords: Portfolio credit derivatives, collateralized debt obligation, inverse problem, local intensity, default intensity, expected tranche notionals, calibration, CDO tranche

JEL Classification: G13, G12

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Date posted: August 13, 2009 ; Last revised: November 14, 2012

Suggested Citation

Cont, Rama and Deguest, Romain and Kan, Yu Hang (Gabriel), Default Intensities implied by CDO Spreads: Inversion Formula and Model Calibration (April 2010). Available at SSRN: http://ssrn.com/abstract=1447979 or http://dx.doi.org/10.2139/ssrn.1447979

Contact Information

Rama Cont (Contact Author)
Imperial College London ( email )
London, SW7 2AZ
United Kingdom
HOME PAGE: http://www3.imperial.ac.uk/people/r.cont
CNRS ( email )
Laboratoire de Probabilites & Modeles aleatoires
Universite Pierre & Marie Curie (Paris VI)
Paris, 75252
HOME PAGE: http://www.proba.jussieu.fr/pageperso/ramacont/
Romain Deguest
Fundvisory ( email )
48 rue du Chateau Landon
Paris, 75010
HOME PAGE: http://www.fundvisory.com/
Yu Hang Kan
Bloomberg Tradebook ( email )
731 Lexington Avenue
New York, NY 10022
United States
Columbia University - Department of Industrial Engineering and Operations Research (IEOR) ( email )
331 S.W. Mudd Building
500 West 120th Street
New York, NY 10027
United States
HOME PAGE: http://www.columbia.edu/~yk2246
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