An expectation-maximization algorithm for the exponential-generalized inverse Gaussian regression model with varying dispersion and shape for modelling the aggregate claim amount

George Tzougas and Himchan Jeong

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: This article presents the Exponential–Generalized Inverse Gaussian regression model with varying dispersion and shape. The EGIG is a general distribution family which, under the adopted modelling framework, can provide the appropriate level of flexibility to fit moderate costs with high frequencies and heavy-tailed claim sizes, as they both represent significant proportions of the total loss in non-life insurance. The model’s implementation is illustrated by a real data application which involves fitting claim size data from a European motor insurer. The maximum likelihood estimation of the model parameters is achieved through a novel Expectation Maximization (EM)-type algorithm that is computationally tractable and is demonstrated to perform satisfactorily.

Keywords: Exponential–Generalized Inverse Gaussian Distribution; EM Algorithm; regression models for the mean; dispersion and shape parameters; non-life insurance; heavy-tailed losses (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Pages: 17 pages
Date: 2021-01-08
New Economics Papers: this item is included in nep-cmp, nep-ecm, nep-ias, nep-ore and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Published in Risks, 8, January, 2021, 9(1), pp. 1-17. ISSN: 2227-9091

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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:108210

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