Skip to main content
Log in

Do correlated defaults matter for CDS premia? An empirical analysis

  • Published:
Review of Derivatives Research Aims and scope Submit manuscript

Abstract

Correlated default factors and systemic risk are clearly priced in credit portfolio securities such as CDOs or index CDSs. In this paper we study an extensive CDX data set for evidence of whether correlated default factors are also present in the underlying CDS market. We develop a cash-flow-based top-down approach for modeling CDSs from which we can derive the following major contributions: (1) Correlated default factors did not matter for CDS prices prior to the financial crisis in 2008. During and after the crisis, however, their importance increased strongly. (2) We observe that correlated default factors primarily impact on the CDS prices of firms with an overall low CDS level. (3) Idiosyncratic risk factors for each single CDS play a major (minor) role when the CDS premia are high (low).

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Notes

  1. The index CDS can be considered as a tranche on the whole portfolio interval 0–1.

  2. The facts also hold for CDS model premia.

  3. Descriptive statistics for the pre-crisis, crisis and post-crisis periods are reported in the “Appendix”.

  4. Descriptive statistics for the pre-crisis, crisis and post-crisis periods are reported in the “Appendix”.

  5. Or even less than \(0\,\%\). In that rare case, two single names default if the first factor jumps.

References

  • Arnsdorf, M., & Halperin, I. (2009). BSLP: Markovian bivariate spread-loss model for portfolio credit derivatives. Journal of Computational Finance, 12, 77–107.

    Google Scholar 

  • Bielecki, T. R., Crépey, S., & Jeanblanc, M. (2010). Up and down credit risk. Quantitative Finance, 10, 1137–1151.

    Article  Google Scholar 

  • Brigo, D., Pallavicini, A., & Torresetti, R. (2010). Credit models and the crisis: A journey into CDOs, copulas,correlations and dynamic models. West Sussex, UK: Wiley Finance.

  • Cox, J. C., Ingersoll, J. E., & Ross, S. A. (1985). A theory of the term structure of interest rates. Econometrica, 53, 385–408.

    Article  Google Scholar 

  • Das, S. R., Duffie, D., Kapadia, N., & Saita, L. (2007). Common failings: How corporate defaults are correlated. Journal of Finance, 62, 93–117.

  • Ding, X., Giesecke, K., & Tomecek, P. I. (2009). Time-changed birth processes and multiname credit derivatives. Operations Research, 57, 990–1005.

    Article  Google Scholar 

  • Duffie, D., Saita, L., & Wang, K. (2007). Multi-period corporate default prediction with stochastic covariates. Journal of Financial Economics, 83, 635–665.

    Article  Google Scholar 

  • Duffie, D., Eckner, A., Horel, G., & Saita, L. (2009). Frailty correlated default. Journal of Finance, 64, 2089–2123.

    Article  Google Scholar 

  • Fama, E. F., & French, K. R. (1992). The cross-section of expected stock returns. Journal of Finance, 47, 427–465.

  • Feldhütter, P., & Nielsen, M. S. (2012). Systematic and idiosyncratic default risk in synthetic credit markets. Journal of Financial Econometrics, 10, 292–324.

    Article  Google Scholar 

  • Gallant, A. R. (1975). Nonlinear regression. The American Statistician, 29, 73–81.

    Google Scholar 

  • Giesecke, K., Goldberg, L. R., & Ding, X. (2011). A top-down approach to multiname credit. Operations Research, 59, 283–300.

  • Gürkaynak, R. S., Sack, B., & Wright, J. H. (2006). The U.S. Treasury yield curve: 1961 to the present, Finance and Economics Discussion Series. Divisions of Research & Statistics and Monetary Affairs, Federal Reserve Board, Washington, DC.

  • Hull, J., & White, A. (2004). Valuation of a CDO and an \(n{\rm th}\) to default CDS without Monte Carlo simulation. Journal of Derivatives, 12, 8–23.

  • Junge, B., & Trolle, A. B. (2015). Liquidity risk in credit default swap markets, Swiss Finance Institute, Working Paper.

  • Kahle, K. M., & Stulz, R. M. (2010). Financial policies and the financial crisis: How important was the systemic credit contraction for industrial corporations? NBER Working Paper Series.

  • Lando, D. (1998). On Cox processes and credit risky securities. Review of Derivatives Research, 2, 99–120.

    Google Scholar 

  • Li, D. X. (2000). On default correlation: A copula function approach. Journal of Fixed Income, 9, 43–54.

    Article  Google Scholar 

  • Longstaff, F. A., & Rajan, A. (2008). An empirical analysis of the pricing of collateralized debt obligations. Journal of Finance, 63, 529–563.

  • Longstaff, F. A., Mithal, S., & Neis, E. (2005). Corporate yield spreads: Default risk or liquidity? New evidence from the credit default swap market. Journal of Finance, 60, 2213–2253.

  • Lopatin, A. V. (2011). A simple dynamic model for pricing and hedging heterogeneous CDOs. In T. R. Bielecki, D. Brigo, & F. Patras (Eds.), Credit risk frontiers, chap 4 (1st ed., pp. 71–103). Hoboken, NJ, USA: Wiley.

    Google Scholar 

  • Meine, C., Supper, H., & Weiß, G. N. F. (2014). Is tail risk priced in credit default swap premia? University of Dortmund, Working Paper.

  • Schönbucher, P. J. (2005). Portfolio losses and the term structure of loss transition rates: A new methodology for the pricing of Portfolio credit derivatives, ETH Zurich, Working Paper.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Philipp Koziol.

Additional information

We have benefited from comments by Peter Raupach, Niels Schulze, participants of the Deutsche Bundesbank Research Seminar and an anonymous referee. This paper represents the authors’ personal opinions and does not necessarily reflect the views of the Deutsche Bundesbank or its staff.

Appendix

Appendix

See Tables 5, 6, 7, 8, 9 and 10.

Table 5 Summary statistics for the levels and first differences of the CDX North America Investment Grade CDO tranche premia during the pre-crisis period
Table 6 Summary statistics for the levels and first differences of the CDX North America Investment Grade CDO tranche premia during the crisis period
Table 7 Summary statistics for the levels and first differences of the CDX North America Investment Grade CDO tranche premia during the post-crisis period
Table 8 Summary statistics for the levels and first differences of the cross section of CDX index constituents during the pre-crisis period
Table 9 Summary statistics for the levels and first differences of the cross section of CDX index constituents during the crisis period
Table 10 Summary statistics for the levels and first differences of the cross section of CDX index constituents during the post-crisis period

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Koziol, C., Koziol, P. & Schön, T. Do correlated defaults matter for CDS premia? An empirical analysis. Rev Deriv Res 18, 191–224 (2015). https://doi.org/10.1007/s11147-015-9109-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11147-015-9109-4

Keywords

JEL Classification

Navigation