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

Yichun Chi, Zuo Quan Xu and Sheng Chao Zhuang

Papers from arXiv.org

Abstract: In this paper, 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 non-concave, these two problems bring highly unconventional and challenging issues for which classical optimization techniques often fail. Using 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.

Date: 2021-08, Revised 2021-12
New Economics Papers: this item is included in nep-isf and nep-rmg
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Journal Article: Distributionally Robust Goal-Reaching Optimization in the Presence of Background Risk (2022)
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