Multiobjective optimization for the asset allocation of European nonlife insurance companies
Bilel Jarraya () and
MPRA Paper from University Library of Munich, Germany
An optimal asset allocation is crucial for non-life insurance companies. The most previous studies focused on this topic use a mono-objective technique optimization. This technique usually allows the maximization of shareholders’ expected utility. As non-life insurance company is a complex system, it has many stakeholders other than shareholders. So, the satisfaction of the shareholders’ expected utility cannot lead usually to the satisfaction of other stakeholders’ objectives. Therefore, the focus on utility maximization can be a destruction source of other objectives such as productivity, competitiveness and solvency. Our developed model integrates simulation approach with a Multi-Objective Particle Swarm Optimization algorithm. This model insures an optimal asset allocation that maximizes, simultaneously, shareholders expected utility and technical efficiency of European non-life insurance companies. The empirical application conducts a comparison between the attained results with multi-objective optimization technique and mono-objective technique to search the optimal asset allocation for non-life insurance companies. Our results show that the investment portfolio will be more diversified between most available investment assets. In addition, any decision maker should take account of different stakeholders’ objectives. Accordingly multi-objective optimization allows us to find the best asset allocation that maximizes simultaneously expected utility and technical efficiency of non-life insurance companies.
Keywords: Simulation; Multi-objective particle swarm optimization; Asset allocation; Technical efficiency; Shareholders expected utility; European non-life insurance companies. (search for similar items in EconPapers)
JEL-codes: G11 G12 G17 G22 (search for similar items in EconPapers)
Date: 2013, Revised 2013
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Published in Journal of Multi-Criteria Decision Analysis 3-4.20(2013): pp. 97-108
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:53697
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