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

Tzougas, George, Hoon, W. L. and Lim, J. M. (2019) The negative binomial-inverse Gaussian regression model with an application to insurance ratemaking. European Actuarial Journal, 9 (1). 323 - 344. ISSN 2190-9733

Text (NBIG_Model_Tzougas LSE) - Accepted Version Download (293kB)

• Author

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.

Item Type:	Article
Official URL:	https://link.springer.com/journal/13385
Additional Information:	© EAJ Association 2018
Divisions:	Statistics
Subjects:	Q Science > QA Mathematics H Social Sciences > HJ Public Finance
Date Deposited:	26 Sep 2019 10:27
Last Modified:	12 Dec 2024 01:53
URI:	http://eprints.lse.ac.uk/id/eprint/101728

Actions (login required)

View Item