Modelling Low Default Portfolios

Master thesis (2018)

Authors

J.C. Zoutendijk Electrical Engineering, Mathematics and Computer Science

Contributors

P. Cirillo (supervisor 1)
Wilco den Dunnen (supervisor 1)
H.P. Lopuhaä (supervisor 1)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2018 Joran Zoutendijk
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Published Date

31-07-2018

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

This thesis is on the subject of modelling the probability of default in a low default portfolio. In these portfolios there is a high risk of underestimating the true probability of default. Two models are considered, a Gaussian one factor model and a Poisson model with Gamma mixture. Classical estimation methods as the maximum likelihood are shown to fail to produce conservative results, and therefore the Bayesian approach is considered. New on the subject is the consideration of multivariate prior distributions, which are shown to be an improvement of the univariate case.