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, Himchan Jeong

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
1 Downloads (Pure)

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.

Original languageEnglish
Article number19
Number of pages17
JournalRisks
Volume9
Issue number1
DOIs
Publication statusPublished - 8 Jan 2021

Keywords

  • EM Algorithm
  • Exponential–Generalized Inverse Gaussian Distribution
  • Heavy-tailed losses
  • Non-life insurance
  • Regression models for the mean, dispersion and shape parameters

ASJC Scopus subject areas

  • Accounting
  • Economics, Econometrics and Finance (miscellaneous)
  • Strategy and Management

Fingerprint

Dive into the research topics of 'An expectation-maximization algorithm for the exponential-generalized inverse gaussian regression model with varying dispersion and shape for modelling the aggregate claim amount'. Together they form a unique fingerprint.

Cite this