Skip navigation

Optimal risk transfer: a numerical optimization approach

Optimal risk transfer: a numerical optimization approach

Asimit, Alexandru V., Gao, Tao ORCID: 0000-0002-6425-1568, Hu, Junlei and Kim, Eun-Seok (2018) Optimal risk transfer: a numerical optimization approach. North American Actuarial Journal, 22 (3). pp. 341-364. ISSN 1092-0277 (doi:

PDF (Author's Accepted Manuscript)
25954 GAO_Optimal_Risk_Transfer_A_Numerical_Optimization_Approach_(AAM)_2018.pdf - Accepted Version

Download (6MB) | Preview


Capital efficiency and asset/liability management are part of the Enterprise Risk Management Process of any insurance/reinsurance conglomerate and serve as quantitative methods to fulfill the strategic planning within an insurance organization. A considerable amount of work has been done in this ample research field, but invariably one of the last questions is whether or not, numerically, the method is practically implementable, which is our main interest. The numerical issues are dependent on the traits of the optimization problem, and therefore we plan to focus on the optimal reinsurance design, which has been a very dynamic topic in the last decade. The existing literature is focused on finding closed-form solutions that are usually possible when economic, solvency, and other constraints are not included in the model. Including these constraints, the optimal contract can be found only numerically. The efficiency of these methods is extremely good for some well-behaved convex problems, such as Second-Order Conic Problems. Specific numerical solutions are provided to better explain the advantages of appropriate numerical optimization methods chosen to solve various risk transfer problems. The stability issues are also investigated together with a case study performed for an insurance group that aims capital efficiency across the entire organization.

Item Type: Article
Uncontrolled Keywords: linear programming, optimal reinsurance/risk transfer, risk measure, second-order conic programming
Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / School / Research Centre / Research Group: Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS)
Faculty of Engineering & Science
Last Modified: 04 Mar 2022 13:06

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics