Predicting bivariate binary rare events responses using generalised extreme value regression model and copula function

Silvia Angela Osmetti, Raffaella Calabrese

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A new bivariate Generalised Linear Model (GLM) is proposed for binary rare events, i.e. binary dependent variables with a very small number of ones. In a bivariate GLM model we suggest the quantile function of the Generalised Extreme Value (GEV) distribution. In this way, the drawback of the underestimation of the probability of the rare event in GLM models is overcome. The dependence between the response variables is modelled by the copula function. We explore different copula functions that provide a rich and flexible class of structures to derive joint distributions for bivariate binary data. Finally, we apply the proposed model to estimate the joint probability of defaults of UK and Italian small and medium enterprises.
Original languageEnglish
Title of host publicationPROGRAMME AND ABSTRACTS 7th International Conference on Computational and Financial Econometrics (CFE 2013) and 6th International Conference of the ERCIM (European Research Consortium for Informatics and Mathematics) Working Group on Computational and Methodological Statistics (ERCIM 2013)
Pages176
Number of pages1
Publication statusPublished - 2013
Event6th International Conference of the ERCIM WG on Computational and Methodological Statistics (ERCIM 2013) - Londra
Duration: 14 Dec 201316 Dec 2014

Conference

Conference6th International Conference of the ERCIM WG on Computational and Methodological Statistics (ERCIM 2013)
CityLondra
Period14/12/1316/12/14

Keywords

  • COPULA
  • generalized extreme value distribution

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