The negative binomial-inverse Gaussian regression model with an application to insurance ratemaking

G. Tzougas, W. L. Hoon, J. M. Lim

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

This paper presents the Negative Binomial-Inverse Gaussian regression model for approximating the number of claims as an alternative to mixed Poisson regression models that have been widely used in various disciplines including actuarial applications. The Negative Binomial-Inverse Gaussian regression model can be considered as a plausible model for highly dispersed claim count data and this is the first time that it is used in a statistical or actuarial context. The main achievement is that we propose a quite simple Expectation-Maximization type algorithm for maximum likelihood estimation of the model. Finally, a real data application using motor insurance data is examined and both the a priori and a posteriori, or Bonus-Malus, premium rates resulting from the Negative Binomial-Inverse Gaussian model are calculated via the net premium principle and compared to those determined by the Negative Binomial Type I and the Poisson-Inverse Gaussian regression models that have been traditionally used for a priori and a posteriori ratemaking.

Original languageEnglish
Pages (from-to)323-344
Number of pages22
JournalEuropean Actuarial Journal
Volume9
Issue number1
Early online date17 Nov 2018
DOIs
Publication statusPublished - 1 Jul 2019

Keywords

  • EM algorithm
  • Motor third party liability insurance
  • Negative binomial-inverse Gaussian regression model
  • Ratemaking

ASJC Scopus subject areas

  • Statistics and Probability
  • Economics and Econometrics
  • Statistics, Probability and Uncertainty

Fingerprint

Dive into the research topics of 'The negative binomial-inverse Gaussian regression model with an application to insurance ratemaking'. Together they form a unique fingerprint.

Cite this