Journal article
Budget-constrained optimal reinsurance design under coherent risk measures
Scandinavian actuarial journal, Vol.2019(9), pp.729-751
10/21/2019
DOI: 10.1080/03461238.2019.1598891
Abstract
Reinsurance is a versatile risk management strategy commonly employed by insurers to optimize their risk profile. In this paper, we study an optimal reinsurance design problem minimizing a general law-invariant coherent risk measure of the net risk exposure of a generic insurer, in conjunction with a general law-invariant comonotonic additive convex reinsurance premium principle and a premium budget constraint. Due to its intrinsic generality, this contract design problem encompasses a wide body of optimal reinsurance models commonly encountered in practice. A three-step solution scheme is presented. Firstly, the objective and constraint functions are exhibited in the so-called Kusuoka's integral representations. Secondly, the mini-max theorem for infinite dimensional spaces is applied to interchange the infimum on the space of indemnities and the supremum on the space of probability measures. Thirdly, the recently developed Neyman-Pearson methodology due to Lo (2017a) is adopted to solve the resulting infimum problem. Analytic and transparent expressions for the optimal reinsurance policy are provided, followed by illustrative examples.
Details
- Title: Subtitle
- Budget-constrained optimal reinsurance design under coherent risk measures
- Creators
- Ka Chun Cheung - University of Hong KongWing Fung Chong - University of Illinois Urbana-ChampaignAmbrose Lo - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Scandinavian actuarial journal, Vol.2019(9), pp.729-751
- Publisher
- Taylor & Francis
- DOI
- 10.1080/03461238.2019.1598891
- ISSN
- 0346-1238
- eISSN
- 1651-2030
- Grant note
- 17324516 / Research Grants Council of the Hong Kong Special Administrative Region, China The University of Iowa Project Codes 257406 and 583115 / University of Illinois 2018-2021 / Centers of Actuarial Excellence
- Language
- English
- Date published
- 10/21/2019
- Academic Unit
- Statistics and Actuarial Science
- Record Identifier
- 9984257634902771
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