Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/258364 
Year of Publication: 
2022
Citation: 
[Journal:] Risks [ISSN:] 2227-9091 [Volume:] 10 [Issue:] 3 [Article No.:] 54 [Publisher:] MDPI [Place:] Basel [Year:] 2022 [Pages:] 1-11
Publisher: 
MDPI, Basel
Abstract: 
While both zero-inflation and the unobserved heterogeneity in risks are prevalent issues in modeling insurance claim counts, determination of Bayesian credibility premium of the claim counts with these features are often demanding due to high computational costs associated with a use of MCMC. This article explores a way to approximate credibility premium for claims frequency that follows a zero-inflated Poisson distribution via variational Bayes approach. Unlike many existing industry benchmarks, the proposed method enables insurance companies to capture both zero-inflation and unobserved heterogeneity of policyholders simultaneously with modest computation costs. A simulation study and an empirical analysis using the LGPIF dataset were conducted and it turned out that the proposed method outperforms many industry benchmarks in terms of prediction performances and computation time. Such results support the applicability of the proposed method in the posterior ratemaking practices.
Subjects: 
approximate credibility premium
claim frequency
posterior ratemaking
variational Bayes
zero-inflated Poisson distribution
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Appears in Collections:

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.