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Modelling credit default swap spreads by means of normal mixtures and copulas

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  • Marco Bee

Abstract

This paper develops a multivariate statistical model for the analysis of credit default swap spreads. Given the large excess kurtosis of the univariate marginal distributions, it is proposed to model them by means of a mixture of distributions. However, the multivariate extension of this methodology is numerically difficult, so that copulas are used to capture the structure of dependence of the data. It is shown how to estimate the parameters of the marginal distributions via the EM algorithm; then the parameters of the copula are estimated and standard errors computed through the nonparametric bootstrap. An application to credit default swap spreads of some European reference entities and extensive simulation results confirm the effectiveness of the method.

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File URL: http://www.tandfonline.com/doi/abs/10.1080/1350486042000218420
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Bibliographic Info

Article provided by Taylor & Francis Journals in its journal Applied Mathematical Finance.

Volume (Year): 11 (2004)
Issue (Month): 2 ()
Pages: 125-146

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Handle: RePEc:taf:apmtfi:v:11:y:2004:i:2:p:125-146

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Related research

Keywords: finite mixture distributions; copula; credit default swap spread; non-parametric bootstrap;

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Cited by:
  1. Chen, Yi-Hsuan & Tu, Anthony H. & Wang, Kehluh, 2008. "Dependence structure between the credit default swap return and the kurtosis of the equity return distribution: Evidence from Japan," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 18(3), pages 259-271, July.
  2. Hayette Gatfaoui, 2010. "Investigating the dependence structure between credit default swap spreads and the U.S. financial market," Annals of Finance, Springer, vol. 6(4), pages 511-535, October.
  3. Marco Bee, 2007. "The asymptotic loss distribution in a fat-tailed factor model of portfolio credit risk," Department of Economics Working Papers 0701, Department of Economics, University of Trento, Italia.

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