Distributionally robust goal-reaching optimization in the presence of background risk

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<title>Distributionally robust goal-reaching optimization in the presence of background risk</title>
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<abstract displayLabel="Summary">In this article, we examine the effect of background risk on portfolio selection and optimal reinsurance design under the criterion of maximizing the probability of reaching a goal. Following the literature, we adopt dependence uncertainty to model the dependence ambiguity between financial risk (or insurable risk) and background risk. Because the goal-reaching objective function is nonconcave, these two problems bring highly unconventional and challenging issues for which classical optimization techniques often fail. Using a quantile formulation method, we derive the optimal solutions explicitly. The results show that the presence of background risk does not alter the shape of the solution but instead changes the parameter value of the solution. Finally, numerical examples are given to illustrate the results and verify the robustness of our solutions.

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<note type="statement of responsibility">Yichun Chi</note>
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<topic>Gerencia de riesgos</topic>
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<topic>Cálculo actuarial</topic>
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<title>North American actuarial journal</title>
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<publisher>Schaumburg : Society of Actuaries, 1997-</publisher>
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<identifier type="issn">1092-0277</identifier>
<identifier type="local">MAP20077000239</identifier>
<part>
<text>12/09/2022 Tomo 26 Número 3 - 2022 , p. 351-382</text>
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