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, 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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| Author: | Christian KubitzaGND |
|---|---|
| URN: | urn:nbn:de:hebis:30:3-382988 |
| Referee: | Gaby SchneiderGND, Anton WakolbingerGND |
| 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 |
| Page Number: | 120 |
| Last Page: | 115 |
| HeBIS-PPN: | 36532826X |
| Institutes: | Informatik und Mathematik / Mathematik |
| Dewey Decimal Classification: | 5 Naturwissenschaften und Mathematik / 51 Mathematik / 510 Mathematik |
| Sammlungen: | Universitätspublikationen |
| Licence (German): |  Deutsches Urheberrecht |
