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Optimal investment strategy to minimize the ruin probability of an insurance company under borrowing constraints

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Author Info
Azcue, Pablo
Muler, Nora
Abstract

We consider that the surplus of an insurance company follows a Cramer-Lundberg process. The management has the possibility of investing part of the surplus in a risky asset. We consider that the risky asset is a stock whose price process is a geometric Brownian motion. Our aim is to find a dynamic choice of the investment policy which minimizes the ruin probability of the company. We impose that the ratio between the amount invested in the risky asset and the surplus should be smaller than a given positive bound a. For instance the case a=1 means that the management cannot borrow money to buy stocks. [Hipp, C., Plum, M., 2000. Optimal investment for insurers. Insurance: Mathematics and Economics 27, 215-228] and [Schmidli, H., 2002. On minimizing the ruin probability by investment and reinsurance. Ann. Appl. Probab. 12, 890-907] solved this problem without borrowing constraints. They found that the ratio between the amount invested in the risky asset and the surplus goes to infinity as the surplus approaches zero, so the optimal strategies of the constrained and unconstrained problems never coincide. We characterize the optimal value function as the classical solution of the associated Hamilton-Jacobi-Bellman equation. This equation is a second-order non-linear integro-differential equation. We obtain numerical solutions for some claim-size distributions and compare our results with those of the unconstrained case.

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Publisher Info
Article provided by Elsevier in its journal Insurance: Mathematics and Economics.

Volume (Year): 44 (2009)
Issue (Month): 1 (February)
Pages: 26-34
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Handle: RePEc:eee:insuma:v:44:y:2009:i:1:p:26-34

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Web page: http://www.elsevier.com/locate/inca/505554

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Related research
Keywords: Cramer-Lundberg process Ruin probability Insurance Portfolio optimization Borrowing constraints Hamilton-Jacobi-Bellman equation;

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This page was last updated on 2009-12-3.


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