Review of Derivatives Research

, Volume 15, Issue 1, pp 25–56 | Cite as

Delta-hedging correlation risk?

  • Areski Cousin
  • Stéphane CrépeyEmail author
  • Yu Hang Kan


While the Gaussian copula model is commonly used as a static quotation device for CDO tranches, its use for hedging is questionable. In particular, the spread delta computed from the Gaussian copula model assumes constant base correlations, whereas we show that the correlations are dynamic and correlated to the index spread. It might therefore be expected that a dynamic model of credit risk, which is able to capture the dependence between the base correlations and the index spread, will have better hedging performances. In this paper, we compare delta hedging of spread risk based on the Gaussian copula model, to the implementation of jump-to-default ratio computed from the dynamic local intensity model. Theoretical and empirical analysis are illustrated by using the market data in both before and after the subprime crisis. We observe that delta hedging of spread risk outperforms the implementation of jump-to-default ratio in the pre-crisis period associated with CDX.NA.IG series 5, and the two strategies have comparable performance for crisis period associated with CDX.NA.IG series 9 and 10. This shows that, although the local intensity model is a dynamic model, it is not sufficient to explain the joint dynamic of the index spread and the base correlations, and a richer dynamic model is required to obtain better hedging results. Moreover, although different specifications of the local intensity can be fitted to the market data equally well, their hedging results can be significant different. This reveals substantial model risk when hedging CDO tranches.


Credit risk CDO Hedging Delta Gaussian copula Local intensity Backtesting 

JEL Classification



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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Areski Cousin
    • 1
  • Stéphane Crépey
    • 2
    Email author
  • Yu Hang Kan
    • 3
  1. 1.Université de Lyon, Université Lyon 1, ISFALyon Cedex 07France
  2. 2.Laboratoire Analyse et ProbabilitésUniversité d’évry Val d’EssonneÉvry CedexFrance
  3. 3.IEOR DepartmentColumbia UniversityNew YorkUSA

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