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  1. Calibration alternatives to logistic regression and their potential for transferring the dispersion of discriminatory power into uncertainties of probabilities of default
    Autor*in: Wosnitza, Jan Henrik
    Erschienen: [2022]
    Verlag:  Deutsche Bundesbank, Frankfurt am Main

    The transformation of credit scores into probabilities of default plays an important role in credit risk estimation. The linear logistic regression has developed into a standard calibration approach in the banking sector. With the advent of machine... mehr

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    Standort:
    Leibniz-Institut für Wirtschaftsforschung Halle, Bibliothek
    Fernleihe:
    keine Fernleihe

    Standort:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signatur:
    DS 12
    Fernleihe:
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    The transformation of credit scores into probabilities of default plays an important role in credit risk estimation. The linear logistic regression has developed into a standard calibration approach in the banking sector. With the advent of machine learning techniques in the discriminatory phase of credit risk models, however, the standard calibration approach is currently under scrutiny again. In particular, the assumptions behind the linear logistic regression provide critics with a target. Previous literature has converted the calibration problem into a regression task without any loss of generality. In this paper, we draw on recent academic results in order to suggest two new one-parametric families of differentiable functions as candidates for this regression. The derivation of these two families of differentiable functions is based on the maximum entropy principle and, thus, they rely on a minimum number of assumptions. We compare the performance of four calibration approaches on a real-world data set and find that one of the new one-parametric families outperforms the linear logistic regression. Furthermore, we develop an approach in order to quantify the part of the general estimation error of probabilities of default that stems from the statistical dispersion of the discriminatory power.

     
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      BibTeX-Format
    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Ebook
    Format: Online
    ISBN: 9783957298706
    Weitere Identifier:
    hdl: 10419/251197
    Schriftenreihe: Discussion paper / Deutsche Bundesbank ; no 2022, 04
    Schlagworte: Calibration; credit score; cumulative accuracy profile; logistic regression; margin of conservatism; probability of default
    Umfang: 1 Online-Ressource (circa 42 Seiten), Illustrationen