Modelling Low Default Portfolios

Master Thesis (2018)
Author(s)

J.C. Zoutendijk (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Contributor(s)

Pasquale Cirillo – Mentor

Wilco den Dunnen – Mentor

Rik Lopuhaä – Mentor

Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright
© 2018 Joran Zoutendijk
More Info
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Publication Year
2018
Language
English
Copyright
© 2018 Joran Zoutendijk
Graduation Date
31-07-2018
Awarding Institution
Delft University of Technology
Faculty
Electrical Engineering, Mathematics and Computer Science
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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.

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