Insurance ratemaking using the Exponential-Lognormal regression model

George Tzougas, Woo Hee Yik and Muhammad Waqar Mustaqeem

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

Abstract: This paper is concerned with presenting the Exponential-Lognormal (ELN) regression model as a competitive alternative to the Pareto, or Exponential-Inverse Gamma, regression model that has been used in a wide range of areas, including insurance ratemaking. This is the first time that the ELN regression model is used in a statistical or actuarial context. The main contribution of the study is that we illustrate how maximum likelihood estimation of the ELN regression model, which does not have a density in closed form, can be accomplished relatively easily via an Expectation-Maximisation type algorithm. A real data application based on motor insurance data is examined in order to emphasise the versatility of the proposed algorithm. Finally, assuming that the number of claims is distributed according to the classic Negative Binomial and Poisson-Inverse Gaussian regression models, both the a priori and a posteriori, or Bonus–Malus, premium rates resulting from the ELN regression model are calculated via the net premium principle and compared to those determined by the Pareto regression model that has been traditionally used for modelling claim sizes.

Keywords: Exponential-Lognormal regression model; EM Algorithm; Motor Third Party Liability Insurance; ratemaking (search for similar items in EconPapers)
JEL-codes: F3 G3 (search for similar items in EconPapers)
Pages: 30 pages
Date: 2019-06-26
New Economics Papers: this item is included in nep-ecm and nep-ias
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Published in Annals of Actuarial Science, 26, June, 2019, pp. 1 - 30. ISSN: 1748-4995

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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:101729

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