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

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Author Info

  • 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.

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File URL: http://128.118.178.162/eps/ri/papers/0306/0306003.pdf
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Bibliographic Info

Paper provided by EconWPA in its series Risk and Insurance with number 0306003.

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Length: 18 pages
Date of creation: 19 Jun 2003
Date of revision:
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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Web page: http://128.118.178.162

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Keywords: credit risk models; cumulative accuracy profile; risk modeling;

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References

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  1. Merton, Robert C., 1973. "On the pricing of corporate debt: the risk structure of interest rates," Working papers 684-73., Massachusetts Institute of Technology (MIT), Sloan School of Management.
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Cited by:
  1. 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.
  2. 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.
  3. 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.
  4. 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.

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