EconPapers    
Economics at your fingertips  
 

Modelling uncertainty in insurance Bonus-Malus premium principles by using a Bayesian robustness approach

Emilio Gomez-deniz and Francisco Vazquez-polo

Journal of Applied Statistics, 2005, vol. 32, issue 7, 771-784

Abstract: When Bayesian models are implemented for a Bonus-Malus System (BMS), a parametric structure, π0 (λ), is normally included in the insurer's portfolio. Following Bayesian sensitivity analysis, it is possible to model the structure function by specifying a class Γ of priors instead of a single prior. This paper examines the ranges of the relativities of the form, [image omitted] Standard and robust Bayesian tools are combined to show how the choice of the prior can affect the relative premiums. As an extension of the paper by Gomez et al. (2002b), our model is developed to the variance premium principle and the class of prior densities extended to ones that are more realistic in an actuarial setting, i.e. classes of generalized moments conditions. The proposed method is illustrated with data from Lemaire (1979). The main aim of the paper is to demonstrate an appropriate methodology to perform a Bayesian sensitivity analysis of the Bonus-Malus of loaded premiums.

Keywords: Bonus-malus; Bayesian robustness; ε-contamination class; generalized moments conditions (search for similar items in EconPapers)
Date: 2005
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.tandfonline.com/doi/abs/10.1080/02664760500079746 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:32:y:2005:i:7:p:771-784

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/CJAS20

DOI: 10.1080/02664760500079746

Access Statistics for this article

Journal of Applied Statistics is currently edited by Robert Aykroyd

More articles in Journal of Applied Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().

 
Page updated 2025-03-20
Handle: RePEc:taf:japsta:v:32:y:2005:i:7:p:771-784