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Volatility relation between credit default swap and stock market: new empirical tests

  • Miroslav MateevEmail author
Article
  • 36 Downloads

Abstract

This paper investigates the relation between volatility of CDS and stock prices using a sample of 109 European investment-grade companies, during the period of January 2012 to January 2016. To analyse the volatility relation between CDS and stock prices and its time persistence, we use the Dynamic Conditional Correlation (DCC) model. We also test the volatility spillover hypothesis and investigate the direction of the spillover effect using the BEKK-GARCH model. We find strong evidence in support of the hypothesis that the volatility of CDS and stock prices across European investment-grade companies can be modelled under the dynamic conditional correlation assumption. When we split the volatility into two components, namely, ARCH-effect (that is, short-run persistence of shocks) and GARCH-effect (that is, long-run persistence), we find that, in general, the persistence of correlation is statistically significant, while the impact of innovations (shocks) on correlation is not. Our tests of the volatility spillover hypothesis provide new evidence that the volatility spillover is bi-directional, with the predominant leadership of the European CDS market over the stock market.

Keywords

Credit default swap iTraxx index Volatility spillover Multivariate GARCH 

JEL Classification

C58 G10 G12 

Notes

Acknowledgements

In the memory of my colleague Dr. Elena Marinova, who was a grood friend and excellent researcher.

References

  1. Baba Y, Engle R F, Kraft D, Kroner K (1990) Multivariate Simultaneous Generalized ARCH. UCSD Department of Economics, unpublished manuscriptGoogle Scholar
  2. Belke A, Gokus C (2014) Volatility patterns of CDS, bond and stock markets before and during the financial crisis: evidence from major financial institutions. Int J Econ Financ 6(7):53–70CrossRefGoogle Scholar
  3. Blanco R, Brennan S, Marsh, I (2004) An empirical analysis of the dynamic relationship between investment-grade bonds and credit default swaps. Banco de España Working Papers, No 401Google Scholar
  4. Bollerslev T (1986) Generalized autoregressive conditional Heteroskedasticity. J Econ 31(February):307–327CrossRefGoogle Scholar
  5. Bollerslev T (1990) Modeling the coherence in short-run nominal exchange rates: a multivariate generalized ARCH model. Rev Econ Stat 72:498–505CrossRefGoogle Scholar
  6. Bollerslev T, Engle R, Wooldridge JM (1988) A capital asset pricing model with time varying Covariances. J Polit Econ 96(1):116–131CrossRefGoogle Scholar
  7. Chevallier J (2012) Econometric analysis of carbon markets. Springer Science+Business Media B.VGoogle Scholar
  8. Coudert V, Gex M (2010) Contagion inside the credit default swap market: the case of the GM and ford crisis in 2005. J Int Financ Mark Inst Money 20(2):109–134CrossRefGoogle Scholar
  9. Engle R (1982) Autoregressive conditional heteroscedasticity with estimates of the variance of UK inflation. Econometrica 50(4):987–1008CrossRefGoogle Scholar
  10. Engle R (2002) Dynamic conditional correlation: a simple class of multivariate generalized autoregressive conditional Heteroskedasticity models. J Bus Econ Stat 20(3):339–350CrossRefGoogle Scholar
  11. Engle R, Sheppard K (2001) Theoretical and Empirical Properties of Dynamic Conditional Correlation Multivariate GARCH, NBER Working Paper, No 8554Google Scholar
  12. Fabozzi F, Cheng X, Chen R (2007) Exploring the components of credit risk in credit default swaps. Financ Res Lett 4(March):10–18CrossRefGoogle Scholar
  13. Figuerola-Ferretti I, Paraskevopoulos I (2010) Pairing market risk and credit risk. Working paper. Carlos III University, MadridGoogle Scholar
  14. ISDA (2008) ISDA Publishes Year-End 2008 Market Survey Results, available at http://www.isda.org/statistics/recent.html, accessed August 17, 2016
  15. ISDA (2009) ISDA Publishes Year-End 2009 Market Survey Results, available at http://www.isda.org/statistics/recent.html, accessed August 17, 2016
  16. Katzke N F (2017). Financial Econometrics Practical, Practical 7: Multi-variate Volatility Modelling available at http://curiousquant.com/ClassNotes/FinMetrics/Practicals/Practical_7/Practical_7.pdf, accessed Sept 08, 2017
  17. Li H, Majerowska E (2006) Stock Market Integration: A Multivariate GARCH Analysis on Poland and Hungary. Kingston upon Thames, Economics Discussion Paper, Kingston University, available at http://eprints.kingston.ac.uk/1628/1/Li-H-1628.pdf, accessed Sept 15, 2017
  18. Longstaff F, Mithal S, Neis E (2005) Corporate yield spreads: default risk or liquidity? New evidence from the credit-default swap market. J Financ 60(5):2213–2253CrossRefGoogle Scholar
  19. Mateev M, Marinova E (2018) Relation between credit default swap spreads and stock prices: a non-linear perspective. J Econ Financ.  https://doi.org/10.1007/s12197-017-9423-9
  20. Meng L, ap Gwilym O, Varas J (2009) Volatility transmission among the CDS, equity, and bond markets. J Fixed Income 18(3):33–46Google Scholar
  21. Minović J (2010) Empirical Analysis of Volatility and Co-movements in Serbian Frontier Financial Market: MGARCH Approach. South East European Journal of Economics and Business 5(1): 39–55, available at https://www.degruyter.com/downl oadpdf/j/jeb.2010.5.issue-1/v10033-010-0004-5/v10033-010-0004-5.pdf, accessed Sept 16, 2017
  22. Ötker-Robe İ, Podpiera J (2010). The Fundamental Determinants of Credit Default Risk for European Large Complex Financial Institutions. IMF Working Paper, WP/10/153Google Scholar
  23. Scheicher M (2008) How Has CDO Market Pricing Changed During the Turmoil? Evidence from CDS Index Tranches. European Central Bank Working Paper Series, No 910, available at https://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp910.pdf?90f5b2182c3959b672dfe94b1ecf6f79, accessed Sept 17, 2017
  24. Schreiber I, Müller G, Klüppelberg C, Wagner N (2012) Equities, credits and volatilities: a multivariate analysis of the European market during the sub-prime crisis. Int Rev Financ Anal 24 (C): 57–65, available at  https://doi.org/10.1016/j.irfa.2012.07.006
  25. Tang D, Yan H (2007) Liquidity and Credit Default Swap Spreads. Working Paper, Kennesaw State University and University of South Carolina, 1–41Google Scholar
  26. Toparli E A, Balcilar M (2016) On the Risk Spillover Across the Oil Market, Stock Market, and the Oil Related CDS Sectors: A Volatility Impulse Response Approach. Proceedings of the Middle East Economic Association, Issue 1, 15th International Conference, available at https://www.dohainstitute.edu.qa/MEEA2016/Downloads/Elif%20Akay%20Toparli_Final.pdf, accessed Oct 09, 2017
  27. Tsay R (2014) Multivariate time series analysis with R and financial applications, First edn. Wiley, HobokenGoogle Scholar
  28. Ural M, Demireli E (2015) Volatility transmission of credit default swap (CDS) risk premiums. Dumlupinar University Journal of Social Sciences 45(July) available at http://dergipark.gov.tr/download/article-file/56094, accessed Oct 09, 2017
  29. Wang P, Moore T (2012) The integration of the credit default swap markets during the US subprime crisis: dynamic correlation analysis. J Int Financ Mark Inst Money 22(1):1–15CrossRefGoogle Scholar
  30. Wang, M., Yao Q. (2005) Modelling multivariate volatilities: an ad-hoc method, working paper, London School of Economics and Political Science and Guanghua School of Management, Peking University, available at: http://stats.lse.ac.uk/q.yao/qyao.links/paper/wy04.pdf, accessed Dec 13, 2017
  31. Zhu H (2004) An empirical comparison of credit spreads between the bond market and the credit default swap market. BIS Working Paper, No. 160Google Scholar

Copyright information

© Academy of Economics and Finance 2019

Authors and Affiliations

  1. 1.American University in the EmiratesDubaiUnited Arab Emirates

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