Catastrophe modeling with a doubly stochastic process : Bayesian inference and applications

This work proposes to employ the (bursty) GLO model from Bingmer et. al (2011) to model the occurrence of tropical cyclones. We develop a Bayesian framework to estimate the parameters of the model and, particularly, empl
This work proposes to employ the (bursty) GLO model from Bingmer et. al (2011) to model the occurrence of tropical cyclones. We develop a Bayesian framework to estimate the parameters of the model and, particularly, employ a Markov chain Monte Carlo algorithm. This also allows us to develop a forecasting framework for future events.
Moreover, we assess the default probability of an insurance company that is exposed to claims that occur according to a GLO process and show that the model is able to substantially improve actuarial risk management if events occur in oscillatory bursts.
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Metadaten
Author:Christian Kubitza
URN:urn:nbn:de:hebis:30:3-382988
Referee:Gaby Schneider, Anton Wakolbinger
Advisor:Gaby Schneider
Document Type:Master's Thesis
Language:English
Date of Publication (online):2015/10/13
Year of first Publication:2015
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Granting Institution:Johann Wolfgang Goethe-Universität
Date of final exam:2015/08/31
Release Date:2015/10/13
Tag:Bayesian Inference; catastrophe modeling; doubly stochastic point process; poisson process; risk theory
Pagenumber:120
Last Page:115
HeBIS PPN:36532826X
Institutes:Mathematik
Dewey Decimal Classification:510 Mathematik
Sammlungen:Universitätspublikationen
Licence (German):License Logo Veröffentlichungsvertrag für Publikationen

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