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.
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Notes
Yet, other studies (see e.g., Tang and Yan 2007) find that liquidity effect on CDS spreads is significant with an estimated liquidity premium similar to those of Treasury bonds and corporate bonds.
Price discovery as defined by Figuerola-Ferretti and Paraskevopoulos (2010) is the process of uncovering the asset’s full information or permanent value. The unobservable permanent price reflects the fundamental value of the underlying asset.
Figuerola-Ferretti and Paraskevopoulos (2010) argue that if the VIX index and iTraxx CDS are cointegrated, price discovery may be considered as a dynamic process in search of an equilibrium state. This requires a sudden adjustment of both indexes to new equilibrium for a given arrival of new information. If both markets do not react to new information in the same manner, one may lead the other. When such a lead-lag relationship appears, the leading market is said to provide price discovery.
Schreiber et al. (2012) investigate conditional variances at aggregate index level, instead of an individual company’s level.
In order to estimate the DCC-GARCH and BEKK-GARCH models, we used the R libraries “rmgarch” (for DCC-GARCH) and “mgarch” (for BEKK-GARCH). To estimate BEKK-GARCH model, we may also use another library available - “MTS” but we decided to use “mgarch” since in the empirical literature there are many examples of using “mgarch” (see e,g., Chevallier 2012).
See Katzke (2017), Financial Econometrics Practical, Practical 7: Multi-variate Volatility Modelling, available at http://curiousquant.com/ClassNotes/FinMetrics/Practicals/Practical_7/Practical_7.pdf
iTraxx indexes are standardized contracts with a reference to a fixed number of obligors with shared characteristics. Investors can be long or short of the index, which is equivalent to being protection sellers or buyers. For more information, refer to Markit iTraxx Europe series 24, available at: https://www.markit.com/NewsInformation/NewsAnnouncementsFile?CMSID=6eeeb28203e94f3f83c83d2a58609ab3.
Our preliminary tests show that the models are not well specified. For this reason, we split the whole data set (January 2008 – January 2016) into two sub samples (periods); our tests show that the second period (January 01, 2012 - January 29, 2016) provides much more reliable results. The first period results are available upon request.
We allow for some of the ARCH/GARCH effects not to be statistically significant in the DCC-GARCH(1,1) model in a way that mostly ARCH effects are not significant but not both ARCH and GARCH effects.
The results reported in Appendix B are available upon request.
In this case we analyse the statistical significance of non-diagonal elements of the GARCH-matrix only at the usual levels of significance of 5% and 10%, that is, we estimate the statistical significance of those coefficients that represent the cross-volatility spillover.
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Acknowledgements
In the memory of my colleague Dr. Elena Marinova, who was a grood friend and excellent researcher.
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Mateev, M. Volatility relation between credit default swap and stock market: new empirical tests. J Econ Finan 43, 681–712 (2019). https://doi.org/10.1007/s12197-018-9467-5
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DOI: https://doi.org/10.1007/s12197-018-9467-5