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An empirical evaluation of structural credit risk models

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  • Nikola A. Tarashev

Abstract

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.

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  • 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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    Cited by:

    1. Sim, Jaehun & Kim, Chae-Soo, 2019. "The value of renewable energy research and development investments with default consideration," Renewable Energy, Elsevier, vol. 143(C), pages 530-539.
    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. Samuel Maurer & Luu Nguyen & Asani Sarkar & Chenyang Wei, 2009. "Financial innovation and corporate default rates," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Proceedings of the IFC Conference on "Measuring financial innovation and its impact", Basel, 26-27 August 2008, volume 31, pages 322-343, Bank for International Settlements.
    4. Jumbe, George, 2023. "Credit Risk Assessment Using Default Models: A Review," OSF Preprints ksb8n, Center for Open Science.
    5. Jayadev M. & Joshy Jacob, 2010. "Default Risk Characteristics of Poll-Based Bond Spreads," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 9(1), pages 51-70, April.
    6. Wilmar Cabrera-Rodríguez & Santiago Segovia-Baquero & Juan Sebastián Mariño-Montaña & Eduardo Yanquen, 2019. "Probabilidad de incumplimiento de entidades financieras colombianas: una aproximación estructural," Borradores de Economia 1097, Banco de la Republica de Colombia.
    7. 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.
    8. María Fernanda Hernández & Juan José Valero & María Bernardette Díaz, 2007. "Perfil de riesgos del sistema bancario venezolano: aplicación de la metodología de stress testing," Monetaria, CEMLA, vol. 0(4), pages 405-452, octubre-d.
    9. Mr. Gianni De Nicolo & Alexander F. Tieman, 2006. "Economic Integration and Financial Stability: A European Perspective," IMF Working Papers 2006/296, International Monetary Fund.
    10. Mr. Stefan W. Schmitz & Michael Sigmund & Ms. Laura Valderrama, 2017. "Bank Solvency and Funding Cost: New Data and New Results," IMF Working Papers 2017/116, International Monetary Fund.
    11. Borio, Claudio, 2006. "Monetary and financial stability: Here to stay?," Journal of Banking & Finance, Elsevier, vol. 30(12), pages 3407-3414, December.
    12. 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.
    13. Wilson Sy, 2007. "A Causal Framework for Credit Default Theory," Research Paper Series 204, Quantitative Finance Research Centre, University of Technology, Sydney.
    14. Abel Rodríguez & Enrique ter Horst & Samuel Malone, 2015. "Bayesian Inference for a Structural Credit Risk Model with Stochastic Volatility and Stochastic Interest Rates," Journal of Financial Econometrics, Oxford University Press, vol. 13(4), pages 839-867.
    15. Claudio Borio & Mathias Drehmann, 2009. "Assessing the risk of banking crises - revisited," BIS Quarterly Review, Bank for International Settlements, March.

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    More about this item

    Keywords

    Basel II; Probability of default; Credit risk models; Macroeconomic factors of credit risk;
    All these keywords.

    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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