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Are Banking Systems Increasingly Fragile ? Investigating Financial Institutions’ CDS Returns Extreme Co-Movements

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Author Info
Dima Rahman

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Abstract

This paper investigates potential contagion among the major financial institutions in developed economies. Using Credit Default Swaps (CDS) premia as a measure of credit or counterparty risk, our analysis focuses on the extreme co-movements of Financial Institutions' default contracts during the high level of stress undergone by the CDS markets in the aftermath of the 2007 sub-prime crisis. Our approach is twofold: first, under different tail dependence scenarios, we calibrate several multivariate linear propagation models of constant correlation. Our Monte Carlo simulation study finds evidence of contagion for Financial Institutions- notably in the US-and captures a non-normal dependence structure in the tails for the traded contracts. Second, we estimate a multivariate Dynamic Conditional Correlation-GARCH (DCC-GARCH) model, and demonstrate significant ARCH and GARCH effects, as well as time-varying correlations in CDS spreads variations. Our overall analysis rejects the assumption of constant correlation. More importantly, it advocates changing structures in tail dependence for CDS series during times of financial turmoil as an important feature of banks’ increased fragility.

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Paper provided by University of Paris West - Nanterre la Défense, EconomiX in its series EconomiX Working Papers with number 2009-34.

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Length: 31 pages
Date of creation: 2009
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Handle: RePEc:drm:wpaper:2009-34

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Related research
Keywords: Bank fragility; Counterparty risk; Financial crises; Extreme co-movements; Conditional correlation; Multivariate GARCH; Monte Carlo simulation;

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  2. Ilan Goldfajn & Taimur Baig, 1999. "Financial market contagion in the Asian crisis," Textos para discussão 400, Department of Economics PUC-Rio (Brazil). [Downloadable!]
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  5. Andrew Ang & Geert Bekaert, 2002. "International Asset Allocation With Regime Shifts," Review of Financial Studies, Oxford University Press for Society for Financial Studies, vol. 15(4), pages 1137-1187.
  6. Ramchand, Latha & Susmel, Raul, 1998. "Volatility and cross correlation across major stock markets," Journal of Empirical Finance, Elsevier, vol. 5(4), pages 397-416, October. [Downloadable!] (restricted)
  7. Tim Bollerslev & Jeffrey Wooldridge, 1992. "Quasi-maximum likelihood estimation and inference in dynamic models with time-varying covariances," Econometric Reviews, Taylor and Francis Journals, vol. 11(2), pages 143-172. [Downloadable!] (restricted)
  8. Francis A. Longstaff & Sanjay Mithal & Eric Neis, 2005. "Corporate Yield Spreads: Default Risk or Liquidity? New Evidence from the Credit Default Swap Market," Journal of Finance, American Finance Association, vol. 60(5), pages 2213-2253, October. [Downloadable!] (restricted)
  9. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-50, July.
  10. Kim, Tae-Hwan & White, Halbert, 2004. "On more robust estimation of skewness and kurtosis," Finance Research Letters, Elsevier, vol. 1(1), pages 56-73, March. [Downloadable!] (restricted)
  11. Kee-Hong Bae & G. Andrew Karolyi & René M. Stulz, 2003. "A New Approach to Measuring Financial Contagion," Review of Financial Studies, Oxford University Press for Society for Financial Studies, vol. 16(3), pages 717-763, July. [Downloadable!] (restricted)
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  12. Jorion, Philippe & Zhang, Gaiyan, 2007. "Good and bad credit contagion: Evidence from credit default swaps," Journal of Financial Economics, Elsevier, vol. 84(3), pages 860-883, June. [Downloadable!] (restricted)
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