IDEAS home Printed from https://ideas.repec.org/p/zbw/sfb475/200433.html
   My bibliography  Save this paper

The cost for the default of a loan : Linking theory and practice

Author

Listed:
  • Sibbertsen, Philipp
  • Weißbach, Rafael

Abstract

When calculating the cost of entering into a credit transaction the predominant stochastic component is the expected loss. Often in the credit business the one-year probability of default of the liable counterpart is the only reliable parameter. We use this probability to calculating the exact expected loss of trades with multiple cash ows. Assuming a constant hazard rate for the default time of the liable counterpart we show that the methodology used in practice is a linear Taylor approximation of our exact calculus. In a second stage we can generalize the calculation to arbitrary hazard rates for which we prove statistical evidence and develop an estimate from historical data.

Suggested Citation

  • Sibbertsen, Philipp & Weißbach, Rafael, 2004. "The cost for the default of a loan : Linking theory and practice," Technical Reports 2004,33, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
  • Handle: RePEc:zbw:sfb475:200433
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/22545/1/tr33-04.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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)

    Citations

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


    Cited by:

    1. Weißbach, Rafael, 2004. "A rule of thumb for the economic capital of a large credit portfolio," Technical Reports 2004,58, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.

    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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. Kiefer, Nicholas M. & Larson, C. Erik, 2007. "A simulation estimator for testing the time homogeneity of credit rating transitions," Journal of Empirical Finance, Elsevier, vol. 14(5), pages 818-835, December.
    7. 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.
    8. 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.
    9. Custodio João, Igor & Lucas, André & Schaumburg, Julia & Schwaab, Bernd, 2023. "Dynamic clustering of multivariate panel data," Journal of Econometrics, Elsevier, vol. 237(2).
    10. François Coppens & Fernando Gonzáles & Gerhard Winkler, 2007. "The performance of credit rating systems in the assessment of collateral used in Eurosystem monetary policy operations," Working Paper Research 118, National Bank of Belgium.
    11. Johannes Hörner & Nicolas S Lambert, 2021. "Motivational Ratings [Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions]," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 88(4), pages 1892-1935.
    12. Lando, David & Mortensen, Allan, 2004. "On the Pricing of Step-Up Bonds in the European Telecom Sector," Working Papers 2004-9, Copenhagen Business School, Department of Finance.
    13. Koopman, Siem Jan & Lucas, Andre & Monteiro, Andre, 2008. "The multi-state latent factor intensity model for credit rating transitions," Journal of Econometrics, Elsevier, vol. 142(1), pages 399-424, January.
    14. Aussenegg, Wolfgang & Resch, Florian & Winkler, Gerhard, 2011. "Pitfalls and remedies in testing the calibration quality of rating systems," Journal of Banking & Finance, Elsevier, vol. 35(3), pages 698-708, March.
    15. Areski Cousin & Mohamed Reda Kheliouen, 2016. "A comparative study on the estimation of factor migration models," Working Papers halshs-01351926, HAL.
    16. Chan, Ngai Hang & Wong, Hoi Ying & Zhao, Jing, 2012. "Structural model of credit migration," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3477-3490.
    17. Mogens Bladt & Michael SØrensen, 2009. "Efficient estimation of transition rates between credit ratings from observations at discrete time points," Quantitative Finance, Taylor & Francis Journals, vol. 9(2), pages 147-160.
    18. Tarashev, Nikola, 2010. "Measuring portfolio credit risk correctly: Why parameter uncertainty matters," Journal of Banking & Finance, Elsevier, vol. 34(9), pages 2065-2076, September.
    19. Orth, Walter, 2012. "The predictive accuracy of credit ratings: Measurement and statistical inference," International Journal of Forecasting, Elsevier, vol. 28(1), pages 288-296.
    20. Posch, Peter N., 2011. "Time to change. Rating changes and policy implications," Journal of Economic Behavior & Organization, Elsevier, vol. 80(3), pages 641-656.

    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:sfb475:200433. 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.