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Measuring the Discriminative Power of Rating Systems

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  • Engelmann, Bernd
  • Hayden, Evelyn
  • Tasche, Dirk

Abstract

Assessing the discriminative power of rating systems is an important question to banks and to regulators. In this article we analyze the Cumulative Accuracy Profile (CAP) and the Receiver Operating Characteristic (ROC) which are both commonly used in practice. We give a test-theoretic interpretation for the concavity of the CAP and the ROC curve and demonstrate how this observation can be used for more efficiently exploiting the informational contents of accounting ratios. Furthermore, we show that two popular summary statistics of these concepts, namely the Accuracy Ratio and the area under the ROC curve, contain the same information and we analyse the statistical properties of these measures. We show in detail how to identify accounting ratios with high discriminative power, how to calculate confidence intervals for the area below the ROC curve, and how to test if two rating models validated on the same data set are different. All concepts are illustrated by applications to real data. --

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Bibliographic Info

Paper provided by Deutsche Bundesbank, Research Centre in its series Discussion Paper Series 2: Banking and Financial Studies with number 2003,01.

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Date of creation: 2003
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Handle: RePEc:zbw:bubdp2:2225

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Related research

Keywords: Validation; Rating Models; Credit Analysis;

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References

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  1. Dirk Tasche, 2002. "Remarks on the monotonicity of default probabilities," Papers cond-mat/0207555, arXiv.org.
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Citations

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Cited by:
  1. Rodriguez, Adolfo & Trucharte, Carlos, 2007. "Loss coverage and stress testing mortgage portfolios: A non-parametric approach," Journal of Financial Stability, Elsevier, vol. 3(4), pages 342-367, December.
  2. Martin Rezac & Frantisek Rezac, 2011. "How to Measure the Quality of Credit Scoring Models," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 61(5), pages 486-507, November.
  3. Edward Altman & Gabriele Sabato, 2005. "Effects of the New Basel Capital Accord on Bank Capital Requirements for SMEs," Journal of Financial Services Research, Springer, vol. 28(1), pages 15-42, October.
  4. Repullo, Rafael & Saurina, Jesús & Trucharte, Carlos, 2009. "Mitigating the Procyclicality of Basel II," CEPR Discussion Papers 7382, C.E.P.R. Discussion Papers.
  5. Laura Auria & Rouslan A. Moro, 2008. "Support Vector Machines (SVM) as a Technique for Solvency Analysis," Discussion Papers of DIW Berlin 811, DIW Berlin, German Institute for Economic Research.
  6. João Fernandes, 2005. "Corporate Credit Risk Modeling: Quantitative Rating System And Probability Of Default Estimation," Finance 0505013, EconWPA.
  7. Dierkes, Maik & Erner, Carsten & Langer, Thomas & Norden, Lars, 2013. "Business credit information sharing and default risk of private firms," Journal of Banking & Finance, Elsevier, vol. 37(8), pages 2867-2878.
  8. Zvika Afik & Ohad Arad & Koresh Galil, 2012. "Using Merton model: an empirical assessment of alternatives," Working Papers 1202, Ben-Gurion University of the Negev, Department of Economics.
  9. Radu Muntean, 2009. "Early Warning Models for Banking Supervision in Romania," Advances in Economic and Financial Research - DOFIN Working Paper Series 39, Bucharest University of Economics, Center for Advanced Research in Finance and Banking - CARFIB.
  10. En-Der Su & Shih-Ming Huang, 2010. "Comparing Firm Failure Predictions Between Logit, KMV, and ZPP Models: Evidence from Taiwan’s Electronics Industry," Asia-Pacific Financial Markets, Springer, vol. 17(3), pages 209-239, September.
  11. Alexandros Benos & George Papanastasopoulos, 2005. "Extending the Merton Model: A Hybrid Approach to Assessing Credit Quality," Finance 0505020, EconWPA, revised 03 Jun 2005.

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