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The Definition of the Grading Scales in Banks’ Internal Rating Systems

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  • A. Foglia
  • S. Iannotti
  • P. Marullo Reedtz

Abstract

type="main" xml:lang="en"> An internal risk rating system can be defined as the process used to classify bank borrowers into categories of different credit riskiness. Most of the related literature has investigated various aspects of this process, but the problem of defining the categories and the distribution of borrowers into the different classes or grades has received rather less attention, other than noting that the number of grades and their dispersion should achieve a meaningful differentiation of risk. An appropriate definition of the grading scale is of primary importance because the probability of default associated to each grade is the key inputs of capital allocation systems at many best-practice banks and is the core of the January 2001’s new proposal of the Basel Committee for the calculation of capital requirements. Statistical techniques such as cluster analysis can help in identifying distinct subgroups of borrowers possessing the same creditworthiness. We use a logit model to estimate individual default probabilities for four categories of borrowers and apply cluster analysis to assign borrowers to each grade. However, since cluster analysis is not a purely mechanical process, but requires examination of the nature of observations and of the objective of clustering, the ultimate choice of the most appropriate grading scale for a given portfolio relies on empirical grounds. A sufficient granularity and an appropriate quantification of risk must be balanced. (J.E.L.: G21, G22, G33)

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  • A. Foglia & S. Iannotti & P. Marullo Reedtz, 2001. "The Definition of the Grading Scales in Banks’ Internal Rating Systems," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 30(3), pages 421-456, November.
  • Handle: RePEc:bla:ecnote:v:30:y:2001:i:3:p:421-456
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    File URL: http://hdl.handle.net/10.1111/1468-0300.00066
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    Cited by:

    1. Krink, Thiemo & Paterlini, Sandra & Resti, Andrea, 2008. "The optimal structure of PD buckets," Journal of Banking & Finance, Elsevier, vol. 32(10), pages 2275-2286, October.
    2. Lyra, M. & Paha, J. & Paterlini, S. & Winker, P., 2010. "Optimization heuristics for determining internal rating grading scales," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2693-2706, November.
    3. Krink, Thiemo & Paterlini, Sandra & Resti, Andrea, 2007. "Using differential evolution to improve the accuracy of bank rating systems," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 68-87, September.

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