IDEAS home Printed from https://ideas.repec.org/a/bis/bisqtr/0703i.html
   My bibliography  Save this article

Measuring portfolio credit risk: modelling versus calibration errors

Author

Listed:
  • Nikola Tarashev
  • Haibin Zhu

Abstract

A model-based assessment of credit risk is subject to both specification and calibration errors. Focusing on a well known credit risk model, we propose a methodology for quantifying the relative importance of alternative sources of such errors and apply this methodology to a large data set. We find that flawed calibration of the model can substantially affect the measured level of portfolio credit risk. By contrast, a model misspecification generally has a limited impact, especially for large, well diversified portfolios.

Suggested Citation

  • Nikola Tarashev & Haibin Zhu, 2007. "Measuring portfolio credit risk: modelling versus calibration errors," BIS Quarterly Review, Bank for International Settlements, March.
  • Handle: RePEc:bis:bisqtr:0703i
    as

    Download full text from publisher

    File URL: http://www.bis.org/publ/qtrpdf/r_qt0703i.pdf
    Download Restriction: no

    File URL: http://www.bis.org/publ/qtrpdf/r_qt0703i.htm
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lopez, Jose A., 2004. "The empirical relationship between average asset correlation, firm probability of default, and asset size," Journal of Financial Intermediation, Elsevier, vol. 13(2), pages 265-283, April.
    2. Merton, Robert C, 1974. "On the Pricing of Corporate Debt: The Risk Structure of Interest Rates," Journal of Finance, American Finance Association, vol. 29(2), pages 449-470, May.
    3. Düllmann, Klaus & Scheicher, Martin & Schmieder, Christian, 2007. "Asset correlations and credit portfolio risk: an empirical analysis," Discussion Paper Series 2: Banking and Financial Studies 2007,13, Deutsche Bundesbank.
    4. Susanne Emmer & Dirk Tasche, 2003. "Calculating credit risk capital charges with the one-factor model," Papers cond-mat/0302402, arXiv.org, revised Jan 2005.
    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. Nikola A. Tarashev & Haibin Zhu, 2007. "Modelling and calibration errors in measures of portfolio credit risk," BIS Working Papers 230, Bank for International Settlements.
    2. Nikola Tarashev & Haibin Zhu, 2008. "Specification and Calibration Errors in Measures of Portfolio Credit Risk: The Case of the ASRF Model," International Journal of Central Banking, International Journal of Central Banking, vol. 4(2), pages 129-173, June.
    3. Klaus Duellmann & Jonathan Küll & Michael Kunisch, 2010. "Estimating asset correlations from stock prices or default rates - which method is superior?," Post-Print hal-00736734, HAL.
    4. Düllmann, Klaus & Kunisch, Michael & Küll, Jonathan, 2008. "Estimating asset correlations from stock prices or default rates: which method is superior?," Discussion Paper Series 2: Banking and Financial Studies 2008,04, Deutsche Bundesbank.
    5. Chamizo, Álvaro & Fonollosa, Alexandre & Novales, Alfonso, 2019. "Forward-looking asset correlations in the estimation of economic capital," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 61(C), pages 264-288.
    6. Simone Varotto, 2008. "An Assessment of the Internal Rating Based Approach in Basel II," ICMA Centre Discussion Papers in Finance icma-dp2008-04, Henley Business School, University of Reading.
    7. Mager, Ferdinand & Schmieder, Christian, 2008. "Stress testing of real credit portfolios," Discussion Paper Series 2: Banking and Financial Studies 2008,17, Deutsche Bundesbank.
    8. Sylvia Gottschalk, 2016. "Entropy and credit risk in highly correlated markets," Papers 1604.07042, arXiv.org.
    9. Duellmann, Klaus & Küll, Jonathan & Kunisch, Michael, 2010. "Estimating asset correlations from stock prices or default rates--Which method is superior?," Journal of Economic Dynamics and Control, Elsevier, vol. 34(11), pages 2341-2357, November.
    10. Schmidt, Rafael & Schmieder, Christian, 2009. "Modelling dynamic portfolio risk using risk drivers of elliptical processes," Insurance: Mathematics and Economics, Elsevier, vol. 44(2), pages 229-244, April.
    11. Gottschalk, Sylvia, 2017. "Entropy measure of credit risk in highly correlated markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 478(C), pages 11-19.
    12. William Gornall & Ilya A. Strebulaev, 2013. "Financing as a Supply Chain: The Capital Structure of Banks and Borrowers," NBER Working Papers 19633, National Bureau of Economic Research, Inc.
    13. Trueck, Stefan & Rachev, Svetlozar T., 2008. "Rating Based Modeling of Credit Risk," Elsevier Monographs, Elsevier, edition 1, number 9780123736833.
    14. Pierpaolo Grippa & Lucyna Gornicka, 2016. "Measuring Concentration Risk - A Partial Portfolio Approach," IMF Working Papers 2016/158, International Monetary Fund.
    15. Byström, Hans, 2017. "The currency composition of firms' balance sheets, asset value correlations, and capital requirements," Global Finance Journal, Elsevier, vol. 34(C), pages 89-99.
    16. Ferreira Filipe, Sara & Grammatikos, Theoharry & Michala, Dimitra, 2016. "Pricing default risk: The good, the bad, and the anomaly," Journal of Financial Stability, Elsevier, vol. 26(C), pages 190-213.
    17. Wolff, Christian & Bams, Dennis & Pisa, Magdalena, 2015. "Credit risk characteristics of US small business portfolios," CEPR Discussion Papers 10889, C.E.P.R. Discussion Papers.
    18. Szabó-Morvai, Ágnes, 2003. "Az új bázeli tőkeszabályozás és a belső minősítésen alapuló megközelítés [The new Basel regulations and an approach based on internal rating]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(10), pages 881-890.
    19. Magdalena Pisa & Dennis Bams & Christian Wolff, 2012. "Modeling default correlation in a US retail loan portfolio," LSF Research Working Paper Series 12-19, Luxembourg School of Finance, University of Luxembourg.
    20. Byström, Hans, 2016. "The Currency Composition of Firms' Balance Sheets and its Effect on Asset Value Correlations and Capital Requirements," Working Papers 2016:1, Lund University, Department of Economics.

    More about this item

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation

    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:bis:bisqtr:0703i. 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: Christian Beslmeisl (email available below). General contact details of provider: https://edirc.repec.org/data/bisssch.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.