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An empirical evaluation of structural credit risk models


  • Nikola A. Tarashev


This paper evaluates empirically the performance of six structural credit risk models by comparing the probabilities of default (PDs) they deliver to ex post default rates. In contrast to previous studies pursuing similar objectives, the paper employs firm-level data and finds that theory-based PDs tend to match closely the actual level of credit risk and to account for its time path. At the same time, nonmodelled macro variables from the financial and real sides of the economy help to substantially improve the forecasts of default rates. The finding suggests that theory-based PDs fail to fully reflect the dependence of credit risk on the business and credit cycles. Most of the upbeat conclusions regarding the performance of the PDs are due to models with endogenous default. For their part, frameworks that assume exogenous default tend to underpredict credit risk. Three borrower characteristics influence materially the predictions of the models: the leverage ratio; the default recovery rate; and the risk-free rate of return.

Suggested Citation

  • Nikola A. Tarashev, 2005. "An empirical evaluation of structural credit risk models," BIS Working Papers 179, Bank for International Settlements.
  • Handle: RePEc:bis:biswps:179

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    References listed on IDEAS

    1. 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.
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    5. 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.
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    7. Amato, Jeffery D. & Furfine, Craig H., 2004. "Are credit ratings procyclical?," Journal of Banking & Finance, Elsevier, vol. 28(11), pages 2641-2677, November.
    8. Anderson, Ronald W. & Sundaresan, Suresh & Tychon, Pierre, 1996. "Strategic analysis of contingent claims," European Economic Review, Elsevier, vol. 40(3-5), pages 871-881, April.
    9. Young Ho Eom, 2004. "Structural Models of Corporate Bond Pricing: An Empirical Analysis," Review of Financial Studies, Society for Financial Studies, vol. 17(2), pages 499-544.
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    Cited by:

    1. Gianni De Nicolo & Alexander F. Tieman, 2006. "Economic Integration and Financial Stability; A European Perspective," IMF Working Papers 06/296, International Monetary Fund.
    2. Wikil Kwak & Yong Shi & Gang Kou, 2012. "Bankruptcy prediction for Korean firms after the 1997 financial crisis: using a multiple criteria linear programming data mining approach," Review of Quantitative Finance and Accounting, Springer, vol. 38(4), pages 441-453, May.
    3. Stefan W. Schmitz & Michael Sigmund & Laura Valderrama, 2017. "Bank Solvency and Funding Cost; New Data and New Results," IMF Working Papers 17/116, International Monetary Fund.
    4. Borio, Claudio & Zhu, Haibin, 2012. "Capital regulation, risk-taking and monetary policy: A missing link in the transmission mechanism?," Journal of Financial Stability, Elsevier, vol. 8(4), pages 236-251.
    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. Wilson Sy, 2007. "A Causal Framework for Credit Default Theory," Research Paper Series 204, Quantitative Finance Research Centre, University of Technology, Sydney.
    7. Borio, Claudio, 2006. "Monetary and financial stability: Here to stay?," Journal of Banking & Finance, Elsevier, vol. 30(12), pages 3407-3414, December.
    8. Claudio Borio & Mathias Drehmann, 2009. "Assessing the risk of banking crises - revisited," BIS Quarterly Review, Bank for International Settlements, March.

    More about this item


    Basel II; Probability of default; Credit risk models; Macroeconomic factors of credit risk;

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • G1 - Financial Economics - - General Financial Markets
    • G3 - Financial Economics - - Corporate Finance and Governance

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