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

Text (Risk Manage Insurance Review - 2022 - Tzougas - The multivariate Poisson‐Generalized Inverse Gaussian claim count (1)) - Published Version Available under License Creative Commons Attribution. Download (1MB)

• Author

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:	12 Dec 2024 03:22
URI:	http://eprints.lse.ac.uk/id/eprint/117197

Actions (login required)

View Item