Cookies?
Library Header Image
LSE Research Online LSE Library Services

The design of an optimal Bonus-Malus System based on the Sichel distribution

Tzougas, George and Frangos, Nicholas (2014) The design of an optimal Bonus-Malus System based on the Sichel distribution. In: Silvestrov, Dmitrii and Martin-Löf, Anders, (eds.) Modern Problems in Insurance Mathematics. EAA Series. Springer International (Firm), Cham, Switzerland. ISBN 9783319066523

[img] Text (The Design of an Optimal Bonus-Malus System Based on the Sichel Distribution) - Accepted Version
Download (175kB)

Abstract

This chapter presents the design of an optimal Bonus-Malus System (BMS) using the Sichel distribution to model the claim frequency distribution. This system is proposed as an alternative to the optimal BMS obtained by the traditional Negative Binomial model [19]. The Sichel distribution has a thicker tail than the Negative Binomial distribution and it is considered as a plausible model for highly dispersed count data. We also consider the optimal BMS provided by the Poisson-Inverse Gaussian distribution (PIG), which is a special case of the Sichel distribution. Furthermore, we develop a generalised BMS that takes into account both the a priori and a posteriori characteristics of each policyholder. For this purpose we consider the generalised additive models for location, scale and shape (GAMLSS) in order to use all available information in the estimation of the claim frequency distribution. Within the framework of the GAMLSS we propose the Sichel GAMLSS for assessing claim frequency as an alternative to the Negative Binomial Type I (NBI) regression model used by Dionne and Vanasse [9, 10]. We also consider the NBI and PIG GAMLSS for assessing claim frequency.

Item Type: Book Section
Additional Information: © 2014 Springer International Publishing Switzerland
Divisions: Statistics
Subjects: H Social Sciences > HG Finance
Q Science > QA Mathematics
Date Deposited: 26 Sep 2019 10:36
Last Modified: 13 Sep 2024 23:35
URI: http://eprints.lse.ac.uk/id/eprint/101731

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics