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Bayesian default probability models

Author

Listed:
  • Petra Andrlíková

    (Institute of Economic Studies, Faculty of Social Sciences, Charles University in Prague, Smetanovo nábreží 6, 111 01 Prague 1, Czech Republic)

Abstract

This paper proposes a methodology for default probability estimation for low default portfolios, where the statistical inference may become troublesome. The author suggests using logistic regression models with the Bayesian estimation of parameters. The piecewise logistic regression model and Box-Cox transformation of credit risk score is used to derive the estimates of probability of default, which extends the work by Neagu et al. (2009). The paper shows that the Bayesian models are more accurate in statistical terms, which is evaluated based on Hosmer-Lemeshow goodness of fit test, Hosmer et al. (2013).

Suggested Citation

  • Petra Andrlíková, 2014. "Bayesian default probability models," Working Papers IES 2014/14, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Apr 2014.
    • Handle: RePEc:fau:wpaper:wp2014_14
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    File URL: http://ies.fsv.cuni.cz/sci/publication/show/id/5088/lang/cs
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    More about this item

    Keywords

    default probability; bayesian analysis; logistic regression; goodness-of-fit;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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