eprintid: 14272 rev_number: 20 eprint_status: archive userid: 5 dir: disk0/00/01/42/72 datestamp: 2017-01-19 10:41:58 lastmod: 2021-08-27 17:28:27 status_changed: 2017-01-19 10:41:58 type: article metadata_visibility: show item_issues_count: 0 creators_name: Pflug, G. creators_name: Timonina-Farkas, A. creators_name: Hochrainer-Stigler, S. creators_id: 1361 creators_id: 7958 creators_orcid: 0000-0001-8215-3550 title: Incorporating model uncertainty into optimal insurance contract design ispublished: pub divisions: prog_risk divisions: prog_rpv abstract: In stochastic optimization models, the optimal solution heavily depends on the selected probability model for the scenarios. However, the scenario models are typically chosen on the basis of statistical estimates and are therefore subject to model error. We demonstrate here how the model uncertainty can be incorporated into the decision making process. We use a nonparametric approach for quantifying the model uncertainty and a minimax setup to find model-robust solutions. The method is illustrated by a risk management problem involving the optimal design of an insurance contract. date: 2017-03-19 date_type: published publisher: Elsevier id_number: 10.1016/j.insmatheco.2016.11.008 creators_browse_id: 229 creators_browse_id: 126 full_text_status: public publication: Insurance: Mathematics and Economics volume: 73 pagerange: 68-74 refereed: TRUE issn: 0167-6687 coversheets_dirty: FALSE fp7_project: no fp7_type: info:eu-repo/semantics/article citation: Pflug, G. ORCID: https://orcid.org/0000-0001-8215-3550 , Timonina-Farkas, A., & Hochrainer-Stigler, S. (2017). Incorporating model uncertainty into optimal insurance contract design. Insurance: Mathematics and Economics 73 68-74. 10.1016/j.insmatheco.2016.11.008 . document_url: https://pure.iiasa.ac.at/id/eprint/14272/1/Model_Ambiguity_Pre_Print.pdf