Cookies?
Library Header Image
LSE Research Online LSE Library Services

Confidence intervals of the premiums of optimal Bonus Malus Systems

Tzougas, George, Karlis, Dimitris and Frangos, Nicholas (2017) Confidence intervals of the premiums of optimal Bonus Malus Systems. Scandinavian Actuarial Journal, 2018 (2). pp. 129-144. ISSN 0346-1238

[img]
Preview
PDF - Accepted Version
Download (463kB) | Preview
Identification Number: 10.1080/03461238.2017.1307267

Abstract

In view of the economic importance of motor third-party liability insurance in developed countries the construction of optimal BMS has been given considerable interest. However, a major drawback in the construction of optimal BMS is that they fail to account for the variability on premium calculations which are treated as point estimates. The present study addresses this issue. Specifically, nonparametric mixtures of Poisson laws are used to construct an optimal BMS with a finite number of classes. The mixing distribution is estimated by nonparametric maximum likelihood (NPML). The main contribution of this paper is the use of the NPML estimator for the construction of confidence intervals for the premium rates derived by updating the posterior mean claim frequency. Furthermore, we advance one step further by improving the performance of the confidence intervals based on a bootstrap procedure where the estimated mixture is used for resampling. The construction of confidence intervals for the individual premiums based on the asymptotic maximum likelihood theory is beneficial for the insurance company as it can result in accurate and effective adjustments to the premium rating policies from a practical point of view.

Item Type: Article
Official URL: http://www.tandfonline.com/toc/sact20/current
Additional Information: © 2017 Taylor & Francis
Divisions: Statistics
Subjects: H Social Sciences > HA Statistics
Date Deposited: 27 Mar 2017 12:53
Last Modified: 14 Sep 2024 07:24
URI: http://eprints.lse.ac.uk/id/eprint/70926

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics