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

George Tzougas, W. L. Hoon and J. M. Lim

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

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.

Keywords: Negative binomial-inverse Gaussian regression model; EM algorithm; Motor third party liability insurance; Ratemaking (search for similar items in EconPapers)
JEL-codes: E6 (search for similar items in EconPapers)
Pages: 22 pages
Date: 2019-07
New Economics Papers: this item is included in nep-ecm, nep-ias, nep-mac, nep-ore and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Published in European Actuarial Journal, July, 2019, 9(1), pp. 323 - 344. ISSN: 2190-9733

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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:101728

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