Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/93916
Title: | Distributionally robust goal-reaching optimization in the presence of background risk | Authors: | Chi, Y Xu, ZQ Zhuang, SC |
Issue Date: | 2022 | Source: | North American actuarial journal, 2022, v. 26, no. 3, p. 351-382 | Abstract: | 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. | Publisher: | Routledge | Journal: | North American actuarial journal | ISSN: | 1092-0277 | EISSN: | 2325-0453 | DOI: | 10.1080/10920277.2021.1966805 | Rights: | © 2021 Society of Actuaries This is an Accepted Manuscript of an article published by Taylor & Francis in North American actuarial journal on 7 Oct 2021 (Published online), available online: http://www.tandfonline.com/10.1080/10920277.2021.1966805. |
Appears in Collections: | Journal/Magazine Article |
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File | Description | Size | Format | |
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Xu_Distributionally_Robust_Goal-Reaching.pdf | Pre-Published version | 618.12 kB | Adobe PDF | View/Open |
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