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The multivariate Poisson-Generalized Inverse Gaussian claim count regression model with varying dispersion and shape parameters

Tzougas, George and Makariou, Despoina ORCID: 0000-0002-9001-2122 (2022) The multivariate Poisson-Generalized Inverse Gaussian claim count regression model with varying dispersion and shape parameters. Risk Management and Insurance Review, 25 (4). 401 - 417. ISSN 1098-1616

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Identification Number: 10.1111/rmir.12224

Abstract

We introduce a multivariate Poisson-Generalized Inverse Gaussian regression model with varying dispersion and shape for modeling different types of claims and their associated counts in nonlife insurance. The multivariate Poisson-Generalized Inverse Gaussian regression model is a general class of models which, under the approach adopted herein, allows us to account for overdispersion and positive correlation between the claim count responses in a flexible manner. For expository purposes, we consider the bivariate Poisson-Generalized Inverse Gaussian with regression structures on the mean, dispersion, and shape parameters. The model's implementation is demonstrated by using bodily injury and property damage claim count data from a European motor insurer. The parameters of the model are estimated via the Expectation-Maximization algorithm which is computationally tractable and is shown to have a satisfactory performance.

Item Type: Article
Official URL: https://onlinelibrary.wiley.com/journal/15406296
Additional Information: © 2022 The Authors
Divisions: CPNSS
Statistics
Subjects: H Social Sciences > HG Finance
H Social Sciences > HF Commerce > HF5601 Accounting
H Social Sciences > HB Economic Theory
JEL classification: C - Mathematical and Quantitative Methods > C0 - General > C00 - General
Date Deposited: 27 Oct 2022 15:27
Last Modified: 08 Apr 2024 04:27
URI: http://eprints.lse.ac.uk/id/eprint/117197

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