Abstract
The correct modeling of default dependence is essential for the valuation of portfolio credit derivatives. However, for the pricing of synthetic CDOs a one-factor Gaussian copula model with constant and equal pairwise correlations for all assets in the reference portfolio has become the standard market model. If this model were a reflection of market opinion there wouldn’t be the implied correlation smile that is observed in the market. The purpose of this paper is to derive a correlation structure from observed CDO tranche spreads. The correlation structure is chosen such that all tranche spreads of the traded CDO can be reproduced. This implied correlation structure can then be used to price off-market tranches with the same underlying as the traded CDO. Using this approach we can significantly reduce the risk to misprice off-market derivatives. Due to the complexity of the optimization problem we apply Evolutionary Algorithms.
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References
Andersen, L., Sidenius, J.: Extensions to the Gaussian copula: Random recovery and random factor loadings. The Journal of Credit Risk 1(1), 29–70 (2004)
Gregory, J., Laurent, J.P.: In the core of correlation. Risk, 87–91 (October 2004)
Hager, S., Schöbel, R.: A note on the correlation smile. Tübinger Diskussionsbeitrag 297 (2005)
Hull, J., White, A.: Valuation of a CDO and an n th to default CDS without Monte-Carlo simulation. The Journal of Derivatives 12(2), 8–23 (2004)
Laurent, J.P., Gregory, J.: Basket default swaps, CDOs and factor copulas. The Journal of Risk 7(4), 103–122 (2005)
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© 2006 Springer-Verlag Berlin Heidelberg
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Hager, S., Schöbel, R. (2006). Deriving the Dependence Structure of Portfolio Credit Derivatives Using Evolutionary Algorithms. In: Alexandrov, V.N., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds) Computational Science – ICCS 2006. ICCS 2006. Lecture Notes in Computer Science, vol 3994. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11758549_50
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DOI: https://doi.org/10.1007/11758549_50
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34385-1
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