Journal article
Bowley-optimal convex-loaded premium principles
Insurance, mathematics & economics, Vol.121, pp.157-180
03/01/2025
DOI: 10.1016/j.insmatheco.2025.01.006
Abstract
This paper contributes to the literature on Stackelberg equilibria (Bowley optima) in monopolistic centralized sequential-move insurance markets in several ways. We consider a class of premium principles defined as expectations of increasing and convex functions of the indemnities. We refer to these as convex-loaded premium principles. Our analysis restricts the ex ante admissible class of indemnity functions to the two most popular and practically relevant classes: the deductible indemnities and the proportional indemnities, both of which satisfy the so-called no-sabotage condition. We study Bowley optimality of premium principles within the class of convex-loaded premium principles, when the indemnity functions are either of the deductible type or of the coinsurance type. Assuming that the policyholder is a risk-averse expected-utility maximizer, while the insurer is a risk-neutral expected-profit maximizer, we find that the expected-value premium principle is Bowley optimal for proportional indemnities, while the stop-loss premium principle is Bowley optimal for deductible indemnities under a mild condition. Methodologically, we introduce a novel dual approach to characterize Bowley optima.
Details
- Title: Subtitle
- Bowley-optimal convex-loaded premium principles
- Creators
- Mario Ghossoub - University of WaterlooBin Li - University of WaterlooBenxuan Shi - University of Waterloo
- Resource Type
- Journal article
- Publication Details
- Insurance, mathematics & economics, Vol.121, pp.157-180
- DOI
- 10.1016/j.insmatheco.2025.01.006
- ISSN
- 0167-6687
- eISSN
- 1873-5959
- Number of pages
- 24
- Grant note
- 12271171 / National Natural Science Foundation of China (501100001809) Natural Sciences and Engineering Research Council of Canada (http://data.elsevier.com/vocabulary/SciValFunders/501100000038) 03961; 04338 / Mario Ghossoub and Bin Li 12271171 / National Natural Science Foundation of China (http://data.elsevier.com/vocabulary/SciValFunders/501100001809) 04338 / Natural Sciences and Engineering Research Council of Canada (501100000038) Society of Actuaries (http://data.elsevier.com/vocabulary/SciValFunders/100008139) Society of Actuaries (100008139) 03961 / Natural Sciences and Engineering Research Council of Canada (501100000038)
- Language
- English
- Date published
- 03/01/2025
- Academic Unit
- Statistics and Actuarial Science
- Record Identifier
- 9985179682102771
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