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

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...

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Bibliographic Details
Published in:Risks (Basel) Vol. 9; no. 1; pp. 1 - 17
Main Authors: Tzougas, George, Jeong, Himchan
Format: Journal Article
Language:English
Published: Basel MDPI 01.01.2021
MDPI AG
Subjects:
Algorithms
Behavior
dispersion and shape parameters
EM Algorithm
Exponential-Generalized Inverse Gaussian Distribution
heavy-tailed losses
Insurance premiums
Life insurance
non-life insurance
Random variables
regression models for the mean
regression models for the mean, dispersion and shape parameters
Reinsurance
ISSN:2227-9091, 2227-9091
Online Access:Get full text
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Summary: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.
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ISSN:2227-9091
2227-9091
DOI:10.3390/risks9010019