Robust Bayesian Analysis of Loss Reserves Data Using the Generalized-t Distribution

Jennifer Chan, S.T. Boris Choy () and Udi Makov
Additional contact information
Udi Makov: University of Haifa

No 196, Research Paper Series from Quantitative Finance Research Centre, University of Technology, Sydney

Abstract: This paper presents a Bayesian approach using Markov chain Monte Carlo methods and the generalized-t (GT) distribution to predict loss reserves for the insurance companies. Existing models and methods cannot cope with irregular and extreme claims and hence do not offer an accurate prediction of loss reserves. To develop a more robust model for irregular claims, this paper extends the conventional normal error distribution to the GT distribution which nests several heavytailed distributions including the Student-t and exponential power distributions. It is shown that the GT distribution can be expressed as a scale mixture of uniforms (SMU) distribution which facilitates model implementation and detection of outliers by using mixing parameters. Different models for the mean function, including the log-ANOVA, log-ANCOVA, state space and threshold models, are adopted to analyze real loss reserves data. Finally, the best model is selected according to the deviance information criterion (DIC).

Keywords: Bayesian approach; state space model; threshold model; scale mixtures of uniform distribution; device information criterion (search for similar items in EconPapers)
Pages: 28 pages
Date: 2007-05-01
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for and nep-rmg
References: Add references at CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
https://www.uts.edu.au/sites/default/files/qfr-archive-02/QFR-rp196.pdf (application/pdf)

Related works:
Journal Article: Robust Bayesian Analysis of Loss Reserves Data Using the Generalized-t Distribution (2008)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:uts:rpaper:196

Access Statistics for this paper

More papers in Research Paper Series from Quantitative Finance Research Centre, University of Technology, Sydney PO Box 123, Broadway, NSW 2007, Australia. Contact information at EDIRC.
Bibliographic data for series maintained by Duncan Ford ().