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

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<dc:creator>Chi, Yichun</dc:creator>
<dc:date>2022-09-12</dc:date>
<dc:description xml:lang="es">Sumario: 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.

</dc:description>
<dc:identifier>https://documentacion.fundacionmapfre.org/documentacion/publico/es/bib/180614.do</dc:identifier>
<dc:language>spa</dc:language>
<dc:rights xml:lang="es">InC - http://rightsstatements.org/vocab/InC/1.0/</dc:rights>
<dc:subject xml:lang="es">Gerencia de riesgos</dc:subject>
<dc:subject xml:lang="es">Cálculo actuarial</dc:subject>
<dc:type xml:lang="es">Artículos y capítulos</dc:type>
<dc:title xml:lang="es">Distributionally robust goal-reaching optimization in the presence of background risk</dc:title>
<dc:relation xml:lang="es">En: North American actuarial journal. - Schaumburg : Society of Actuaries, 1997- = ISSN 1092-0277. - 12/09/2022 Tomo 26 Número 3 - 2022 , p. 351-382</dc:relation>
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