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Empirical Analysis of Credit Risk Regime Switching and Temporal Conditional Default Correlation in Credit Default Swap Valuation: The Market liquidity effect

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Author Info

  • Kwamie Dunbar

    (University of Connecticut, Stamford, and Sacread Heart University)

  • Albert J. Edwards

    (Northeast Utilities Service Company)

Abstract

In this paper, we extend the debate concerning Credit Default Swap valuation to include time varying correlation and co-variances. Traditional multi-variate techniques treat the correlations between covariates as constant over time; however, this view is not supported by the data. Secondly, since financial data does not follow a normal distribution because of its heavy tails, modeling the data using a Generalized Linear model (GLM) incorporating copulas emerge as a more robust technique over traditional approaches. This paper also includes an empirical analysis of the regime switching dynamics of credit risk in the presence of liquidity by following the general practice of assuming that credit and market risk follow a Markov process. The study was based on Credit Default Swap data obtained from Bloomberg that spanned the period January 1st 2004 to August 08th 2006. The empirical examination of the regime switching tendencies provided quantitative support to the anecdotal view that liquidity decreases as credit quality deteriorates. The analysis also examined the joint probability distribution of the credit risk determinants across credit quality through the use of a copula function which disaggregates the behavior embedded in the marginal gamma distributions, so as to isolate the level of dependence which is captured in the copula function. The results suggest that the time varying joint correlation matrix performed far superior as compared to the constant correlation matrix; the centerpiece of linear regression models.

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Bibliographic Info

Paper provided by University of Connecticut, Department of Economics in its series Working papers with number 2007-10.

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Length: 37 pages
Date of creation: Apr 2007
Date of revision:
Handle: RePEc:uct:uconnp:2007-10

Note: We are extremely grateful to Ben Fine, Department of Mathematics, Fairfield University, for his many insightful suggestions and guidance which helped to improve the final manuscript. This is a preprint of an article submitted for consideration in the Journal of Empirical Finance.
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Related research

Keywords: Credit Default Swaps; Market Liquidity; Copulas; Joint conditional distributions; Markov process; Regime Switching; Illiquidity; and Correlation.;

This paper has been announced in the following NEP Reports:

References

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  1. Elisa Luciano, 2007. "Calibrating risk-neutral default correlation," Journal of Risk Finance, Emerald Group Publishing, vol. 8(5), pages 450-464, November.
  2. Madan, Dilip & Unal, Haluk, 2000. "A Two-Factor Hazard Rate Model for Pricing Risky Debt and the Term Structure of Credit Spreads," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 35(01), pages 43-65, March.
  3. Darrel Duffie & Leandro Saita & Ke Wang, 2005. "Multi-Period Corporate Default Prediction With Stochastic Covariates," CIRJE F-Series CIRJE-F-373, CIRJE, Faculty of Economics, University of Tokyo.
  4. Long Chen & David A. Lesmond & Jason Wei, 2007. "Corporate Yield Spreads and Bond Liquidity," Journal of Finance, American Finance Association, vol. 62(1), pages 119-149, 02.
  5. Andrew J. Patton, 2006. "Estimation of multivariate models for time series of possibly different lengths," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(2), pages 147-173.
  6. Andrew Patton, 2004. "Modelling Asymmetric Exchange Rate Dependence," Working Papers wp04-04, Warwick Business School, Finance Group.
  7. Cox, John C & Ingersoll, Jonathan E, Jr & Ross, Stephen A, 1985. "A Theory of the Term Structure of Interest Rates," Econometrica, Econometric Society, vol. 53(2), pages 385-407, March.
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Cited by:
  1. 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.

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