IDEAS home Printed from https://ideas.repec.org/p/zbw/sfb475/200546.html
   My bibliography  Save this paper

Kolmogorov-Smirnov-type testing for the partial homogeneity of Markov processes - with application to credit risk

Author

Listed:
  • Weißbach, Rafael
  • Dette, Holger

Abstract

In banking the default behavior of the counterpart is of interest not only for the pricing of transactions under credit risk but also for the assessment of portfolio credit risk. We develop a test against the hypothesis that default intensities are constant over time within a homogeneous group of counterparts under investigation, e.g. a rating class. The Kolmogorov-Smirnov-type test builds on the asymptotic normality of counting processes in event history analysis. Right-censoring accommodates for Markov process with more than one no-absorbing state. A simulation study and an example of rating migrations support the usefulness of the test.

Suggested Citation

  • Weißbach, Rafael & Dette, Holger, 2005. "Kolmogorov-Smirnov-type testing for the partial homogeneity of Markov processes - with application to credit risk," Technical Reports 2005,46, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
  • Handle: RePEc:zbw:sfb475:200546
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/22639/1/tr46-05.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nickell, Pamela & Perraudin, William & Varotto, Simone, 2000. "Stability of rating transitions," Journal of Banking & Finance, Elsevier, vol. 24(1-2), pages 203-227, January.
    2. Duffie, Darrell & Singleton, Kenneth J, 1999. "Modeling Term Structures of Defaultable Bonds," Review of Financial Studies, Society for Financial Studies, vol. 12(4), pages 687-720.
    3. Lando, David & Skodeberg, Torben M., 2002. "Analyzing rating transitions and rating drift with continuous observations," Journal of Banking & Finance, Elsevier, vol. 26(2-3), pages 423-444, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Pesaran, M. Hashem & Schuermann, Til & Treutler, Bjorn-Jakob & Weiner, Scott M., 2006. "Macroeconomic Dynamics and Credit Risk: A Global Perspective," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 38(5), pages 1211-1261, August.
    2. Trueck, Stefan & Rachev, Svetlozar T., 2008. "Rating Based Modeling of Credit Risk," Elsevier Monographs, Elsevier, edition 1, number 9780123736833.
    3. Brian BARNARD, 2017. "Rating Migration and Bond Valuation: Decomposing Rating Migration Matrices from Market Data via Default Probability Term Structures," Expert Journal of Finance, Sprint Investify, vol. 5(1), pages 49-72.
    4. Antje Berndt & Rohan Douglas & Darrell Duffie & Mark Ferguson, "undated". "Measuring Default Risk Premia from Default Swap Rates and EDFs," GSIA Working Papers 2006-E31, Carnegie Mellon University, Tepper School of Business.
    5. Feng, D. & Gourieroux, C. & Jasiak, J., 2008. "The ordered qualitative model for credit rating transitions," Journal of Empirical Finance, Elsevier, vol. 15(1), pages 111-130, January.
    6. Brian BARNARD, 2018. "Rating Migration and Bond Valuation: Ahistorical Interest Rate and Default Probability Term Structures," Expert Journal of Finance, Sprint Investify, vol. 6(1), pages 16-30.
    7. Brian BARNARD, 2017. "Rating Migration and Bond Valuation: Decomposing Rating Migration Matrices from Market Data via Default Probability Term Structures," Expert Journal of Finance, Sprint Investify, vol. 5, pages 49-72.
    8. Schechtman, Ricardo, 2013. "Default matrices: A complete measurement of banks’ consumer credit delinquency," Journal of Financial Stability, Elsevier, vol. 9(4), pages 460-474.
    9. Linda Allen & Anthony Saunders, 2004. "Incorporating Systemic Influences Into Risk Measurements: A Survey of the Literature," Journal of Financial Services Research, Springer;Western Finance Association, vol. 26(2), pages 161-191, October.
    10. Siem Jan Koopman & André Lucas & Pieter Klaassen, 2002. "Pro-Cyclicality, Empirical Credit Cycles, and Capital Buffer Formation," Tinbergen Institute Discussion Papers 02-107/2, Tinbergen Institute.
    11. Correa, Arnildo & Marins, Jaqueline & Neves, Myrian & da Silva, Antonio Carlos, 2014. "Credit Default and Business Cycles: An Empirical Investigation of Brazilian Retail Loans," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 68(3), September.
    12. Figlewski, Stephen & Frydman, Halina & Liang, Weijian, 2012. "Modeling the effect of macroeconomic factors on corporate default and credit rating transitions," International Review of Economics & Finance, Elsevier, vol. 21(1), pages 87-105.
    13. Pesaran M.H. & Schuermann T. & Weiner S.M., 2004. "Modeling Regional Interdependencies Using a Global Error-Correcting Macroeconometric Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 129-162, April.
    14. Georges Dionne & Geneviève Gauthier & Khemais Hammami & Mathieu Maurice & Jean‐Guy Simonato, 2010. "Default Risk in Corporate Yield Spreads," Financial Management, Financial Management Association International, vol. 39(2), pages 707-731, June.
    15. Myriam Ben Ayed & Adel Karaa & Jean‐Luc Prigent, 2018. "Duration Models For Credit Rating Migration: Evidence From The Financial Crisis," Economic Inquiry, Western Economic Association International, vol. 56(3), pages 1870-1886, July.
    16. André Lucas & Siem Jan Koopman, 2005. "Business and default cycles for credit risk," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(2), pages 311-323.
    17. Valerio Vacca, 2011. "An unexpected crisis? Looking at pricing effectiveness of different banks," Temi di discussione (Economic working papers) 814, Bank of Italy, Economic Research and International Relations Area.
    18. Duffie, Darrell & Saita, Leandro & Wang, Ke, 2007. "Multi-period corporate default prediction with stochastic covariates," Journal of Financial Economics, Elsevier, vol. 83(3), pages 635-665, March.
    19. Jones, Stewart & Johnstone, David & Wilson, Roy, 2015. "An empirical evaluation of the performance of binary classifiers in the prediction of credit ratings changes," Journal of Banking & Finance, Elsevier, vol. 56(C), pages 72-85.
    20. Wozabal, David & Hochreiter, Ronald, 2012. "A coupled Markov chain approach to credit risk modeling," Journal of Economic Dynamics and Control, Elsevier, vol. 36(3), pages 403-415.

    More about this item

    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:zbw:sfb475:200546. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/isdorde.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.