IDEAS home Printed from https://ideas.repec.org/p/hum/wpaper/sfb649dp2012-063.html
   My bibliography  Save this paper

Common factors in credit defaults swaps markets

Author

Listed:
  • Yi-Hsuan Chen
  • Wolfgang Karl Härdle

Abstract

We examine what are common factors that determine systematic credit risk and estimate and interpret the common risk factors. We also compare the contributions of common factors in explaining the changes of credit default swap (CDS) spreads during the pre-crisis, crisis and post-crisis period. Based on the testing result from the common principal components model, this study finds that the eigenstructures across the three subperiods are distinct and the determinants of risk factors differ from three subperiods. Furthermore, we analyze the predictive ability of dynamics in CDS indices changes by dynamic factor models.

Suggested Citation

  • Yi-Hsuan Chen & Wolfgang Karl Härdle, 2012. "Common factors in credit defaults swaps markets," SFB 649 Discussion Papers SFB649DP2012-063, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  • Handle: RePEc:hum:wpaper:sfb649dp2012-063
    as

    Download full text from publisher

    File URL: http://sfb649.wiwi.hu-berlin.de/papers/pdf/SFB649DP2012-063.pdf
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    2. Darrell Duffie & Andreas Eckner & Guillaume Horel & Leandro Saita, 2009. "Frailty Correlated Default," Journal of Finance, American Finance Association, vol. 64(5), pages 2089-2123, October.
    3. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    4. Ross, Stephen A., 1976. "The arbitrage theory of capital asset pricing," Journal of Economic Theory, Elsevier, vol. 13(3), pages 341-360, December.
    5. Antonio Di Cesare & Giovanni Guazzarotti, 2010. "An analysis of the determinants of credit default swap spread changes before and during the subprime financial turmoil," Temi di discussione (Economic working papers) 749, Bank of Italy, Economic Research and International Relations Area.
    6. Cremers, Martijn & Driessen, Joost & Maenhout, Pascal & Weinbaum, David, 2008. "Individual stock-option prices and credit spreads," Journal of Banking & Finance, Elsevier, vol. 32(12), pages 2706-2715, December.
    7. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    8. Ericsson, Jan & Jacobs, Kris & Oviedo, Rodolfo, 2009. "The Determinants of Credit Default Swap Premia," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 44(01), pages 109-132, February.
    9. Driessen, J.J.A.G. & Melenberg, B. & Nijman, T.E., 2003. "Common factors in international bond returns," Other publications TiSEM 06a83942-b625-4d3c-808c-a, Tilburg University, School of Economics and Management.
    10. Christoph Benkert, 2004. "Explaining credit default swap premia," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 24(1), pages 71-92, January.
    11. Eichengreen, Barry & Mody, Ashoka & Nedeljkovic, Milan & Sarno, Lucio, 2012. "How the Subprime Crisis went global: Evidence from bank credit default swap spreads," Journal of International Money and Finance, Elsevier, vol. 31(5), pages 1299-1318.
    12. Jun Pan & Kenneth J. Singleton, 2008. "Default and Recovery Implicit in the Term Structure of Sovereign "CDS" Spreads," Journal of Finance, American Finance Association, vol. 63(5), pages 2345-2384, October.
    13. Cao, Charles & Yu, Fan & Zhong, Zhaodong, 2010. "The information content of option-implied volatility for credit default swap valuation," Journal of Financial Markets, Elsevier, vol. 13(3), pages 321-343, August.
    14. Longstaff, Francis A & Schwartz, Eduardo S, 1995. " A Simple Approach to Valuing Risky Fixed and Floating Rate Debt," Journal of Finance, American Finance Association, vol. 50(3), pages 789-819, July.
    15. Sanjiv R. Das & Darrell Duffie & Nikunj Kapadia & Leandro Saita, 2007. "Common Failings: How Corporate Defaults Are Correlated," Journal of Finance, American Finance Association, vol. 62(1), pages 93-117, February.
    16. Driessen, Joost & Melenberg, Bertrand & Nijman, Theo, 2003. "Common factors in international bond returns," Journal of International Money and Finance, Elsevier, vol. 22(5), pages 629-656, October.
    17. Bai, Jushan & Ng, Serena, 2006. "Evaluating latent and observed factors in macroeconomics and finance," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 507-537.
    18. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
    19. Pierre Collin-Dufresne, 2001. "The Determinants of Credit Spread Changes," Journal of Finance, American Finance Association, vol. 56(6), pages 2177-2207, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Xiu Xu & Wolfgang K. Härdle & Cathy Yi-Hsuan Chen, 2016. "Dynamic credit default swaps curves in a network topology," SFB 649 Discussion Papers SFB649DP2016-059, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    2. Ostap Okhrin & Stefan Trück, 2015. "Editorial to the special issue on Applicable semiparametrics of computational statistics," Computational Statistics, Springer, vol. 30(3), pages 641-646, September.

    More about this item

    Keywords

    credit default swaps; common factors; credit risk;

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Interest Rates: Determination, Term Structure, and Effects

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hum:wpaper:sfb649dp2012-063. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (RDC-Team). General contact details of provider: http://edirc.repec.org/data/sohubde.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.