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

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  • Gianluca Oderda
  • Michel M. Dacorogna
  • Tobias Jung

Abstract

We develop a framework to assess the statistical significance of expected default frequency calculated by credit risk models. This framework is then used to analyse the quality of two commercially available models that have become popular among practitioners: KMV Credit Monitor and RiskCalc from Moody's. Copyright Banca Monte dei Paschi di Siena SpA, 2003

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Bibliographic Info

Article provided by Banca Monte dei Paschi di Siena SpA in its journal Economic Notes.

Volume (Year): 32 (2003)
Issue (Month): 2 (07)
Pages: 177-195

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Handle: RePEc:bla:ecnote:v:32:y:2003:i:2:p:177-195

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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. 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.
  2. Stefan Denzler & Michel M. Dacorogna & Ulrich A. Mueller & Alexander McNeil, 2005. "From Default Probabilities To Credit Spreads: Credit Risk Models Do Explain Market Prices," Finance 0504011, EconWPA.
  3. Nidhi Aggarwal & Manish Singh & Susan Thomas, 2012. "Do changes in distance-to-default anticipate changes in the credit rating?," Working Papers 2012-010, Madras School of Economics,Chennai,India.
  4. 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.

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