Skew mixture models for loss distributions: a Bayesian approach

Mauro Bernardi, Antonello Maruotti and Lea Petrella

MPRA Paper from University Library of Munich, Germany

Abstract: The derivation of loss distribution from insurance data is a very interesting research topic but at the same time not an easy task. To find an analytic solution to the loss distribution may be mislading although this approach is frequently adopted in the actuarial literature. Moreover, it is well recognized that the loss distribution is strongly skewed with heavy tails and present small, medium and large size claims which hardly can be fitted by a single analytic and parametric distribution. Here we propose a finite mixture of Skew Normal distributions that provides a better characterization of insurance data. We adopt a Bayesian approach to estimate the model, providing the likelihood and the priors for the all unknow parameters; we implement an adaptive Markov Chain Monte Carlo algorithm to approximate the posterior distribution. We apply our approach to a well known Danish fire loss data and relevant risk measures, as Value-at-Risk and Expected Shortfall probability, are evaluated as well.

Keywords: Markov chain Monte Carlo; Bayesian analysis; mixture model; Skew-Normal distributions; Loss distribution; Danish data (search for similar items in EconPapers)
JEL-codes: C01 C11 C52 (search for similar items in EconPapers)
Date: 2012
New Economics Papers: this item is included in nep-ecm and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (25)

Downloads: (external link)
https://mpra.ub.uni-muenchen.de/39826/1/MPRA_paper_39826.pdf original version (application/pdf)
https://mpra.ub.uni-muenchen.de/40883/1/MPRA_paper_40883.pdf revised version (application/pdf)

Related works:
Journal Article: Skew mixture models for loss distributions: A Bayesian approach (2012)
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:pra:mprapa:39826

Access Statistics for this paper

More papers in MPRA Paper from University Library of Munich, Germany Ludwigstraße 33, D-80539 Munich, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Joachim Winter ().