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Credit Risk Models - Do They Deliver Their Promises? A Quantitative Assessment

Author

Listed:
  • Michel Dacorogna

    (Converium)

  • Gianluca Oderda

    (Pictet et Cie)

  • Tobias Jung

    (Zurich Financial Services)

Abstract

We develop a framework to assess the statistical significance of expected default frequency as calculated by credit risk models. This framework is then used to analyze the quality of two commercially available models that have become popular among practitioners: KMV Credit Monitor and RiskCalc from Moody's. Using a unique database of expected default probability from both vendors, we study both the consistency of predictions and their timeliness. We introduce the concept of cumulative accuracy profile (CAP), which allows to see in one curve the percentage of companies whose defualts were captured by the models one year in advance. We also use the Miller's information test to see if the models add information to the S&P rating. The result of the analysis indicates that these models indeed add relevant information not accounted for by rating alone. Moreover, with respect to rating agencies, the models predict defaults more than ten months in advance on average.

Suggested Citation

  • Michel Dacorogna & Gianluca Oderda & Tobias Jung, 2003. "Credit Risk Models - Do They Deliver Their Promises? A Quantitative Assessment," Risk and Insurance 0306003, EconWPA.
  • Handle: RePEc:wpa:wuwpri:0306003
    Note: Type of Document - Acrobat PDF; prepared on IBM PC; to print on HP A4; pages: 18 ; figures: included
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    References listed on IDEAS

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    1. 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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    Cited by:

    1. Agarwal, Vineet & Taffler, Richard, 2008. "Comparing the performance of market-based and accounting-based bankruptcy prediction models," Journal of Banking & Finance, Elsevier, vol. 32(8), pages 1541-1551, August.
    2. repec:eee:finana:v:55:y:2018:i:c:p:196-208 is not listed on IDEAS
    3. Denzler, Stefan M. & Dacorogna, Michel M. & Muller, Ulrich A. & McNeil, Alexander J., 2006. "From default probabilities to credit spreads: Credit risk models do explain market prices," Finance Research Letters, Elsevier, vol. 3(2), pages 79-95, June.
    4. Marta Gómez-Puig & Simón Sosvilla-Rivero & Manish K. Singh, 2015. "“Sovereigns and banks in the euro area: a tale of two crises”," IREA Working Papers 201504, University of Barcelona, Research Institute of Applied Economics, revised Jan 2015.
    5. Singh, Manish K. & Gómez-Puig, Marta & Sosvilla-Rivero, Simón, 2015. "Bank risk behavior and connectedness in EMU countries," Journal of International Money and Finance, Elsevier, vol. 57(C), pages 161-184.
    6. Li, Ming-Yuan Leon & Miu, Peter, 2010. "A hybrid bankruptcy prediction model with dynamic loadings on accounting-ratio-based and market-based information: A binary quantile regression approach," Journal of Empirical Finance, Elsevier, vol. 17(4), pages 818-833, September.
    7. Marta Gómez-Puig & Simón Sosvilla-Rivero & Manish K. Singh, 2018. "“Incorporating creditors' seniority into contingent claim models:Application to peripheral euro area countries”," IREA Working Papers 201803, University of Barcelona, Research Institute of Applied Economics, revised Feb 2018.
    8. Nidhi Aggarwal & Manish Singh & Susan Thomas, 2012. "Do changes in distance-to-default anticipate changes in the credit rating?," Indira Gandhi Institute of Development Research, Mumbai Working Papers 2012-010, Indira Gandhi Institute of Development Research, Mumbai, India.
    9. Manish K. Singh & Marta Gómez-Puig & Simón Sosvilla-Rivero, 2014. "Forward looking banking stress in EMU countries," Working Papers 14-10, Asociación Española de Economía y Finanzas Internacionales.

    More about this item

    Keywords

    credit risk models; cumulative accuracy profile; risk modeling;

    JEL classification:

    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies

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