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Generalized Extreme Value Regression for Binary Rare Events Data: an Application to Credit Defaults

Author

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  • Raffaella Calabrese

    (Geary Institute, University College Dublin)

  • Silvia Angela Osmetti

    (Department of Statistics, University Cattolica del Dacro Cuore, Milan)

Abstract

The most used regression model with binary dependent variable is the logistic regression model. When the dependent variable represents a rare event, the logistic regression model shows relevant drawbacks. In order to overcome these drawbacks we propose the Generalized Extreme Value (GEV) regression model. In particular, in a Generalized Linear Model (GLM) with binary dependent variable we suggest the quantile function of the GEV distribution as link function, so our attention is focused on the tail of the response curve for values close to one. The estimation procedure is the maximum likelihood method. This model accommodates skewness and it presents a generalization of GLMs with log-log link function. In credit risk analysis a pivotal topic is the default probability estimation. Since defaults are rare events, we apply the GEV regression to empirical data on Italian Small and Medium Enterprises (SMEs) to model their default probabilities.

Suggested Citation

  • Raffaella Calabrese & Silvia Angela Osmetti, 2011. "Generalized Extreme Value Regression for Binary Rare Events Data: an Application to Credit Defaults," Working Papers 201120, Geary Institute, University College Dublin.
    • Handle: RePEc:ucd:wpaper:201120
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    References listed on IDEAS

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    Cited by:

    1. Eleonora Bartoloni & Maurizio Baussola, 2014. "Financial Performance in Manufacturing Firms: A Comparison Between Parametric and Non-Parametric Approaches," Business Economics, Palgrave Macmillan;National Association for Business Economics, vol. 49(1), pages 32-45, January.
    2. Raffaella Calabrese, 2011. "Cost-sensitive classification for rare events: an application to the credit rating model validation for SMEs," Working Papers 201134, Geary Institute, University College Dublin.
    3. Raffaella Calabrese, 2012. "Improving Classifier Performance Assessment of Credit Scoring Models," Working Papers 201204, Geary Institute, University College Dublin.
    4. Laudagé, Christian & Desmettre, Sascha & Wenzel, Jörg, 2019. "Severity modeling of extreme insurance claims for tariffication," Insurance: Mathematics and Economics, Elsevier, vol. 88(C), pages 77-92.

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