{"id":"W4210368301","doi":"10.1287/moor.2021.1217","title":"Inf-Convolution, Optimal Allocations, and Model Uncertainty for Tail Risk Measures","year":2022,"lang":"en","type":"article","venue":"Mathematics of Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Risk measure; Coherent risk measure; Expected shortfall; Tail risk; Dynamic risk measure; Mathematics; Convexity; Measure (data warehouse); Quantile; Convolution (computer science); Econometrics; Value at risk; Risk management; Spectral risk measure; Pareto principle; Downside risk; Mathematical optimization; Computer science; Economics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.01100262,0.00009602644,0.0002246803,0.0006038889,0.001761873,0.0002559104,0.0005706121,0.00004557279,0.0001620604],"category_scores_gemma":[0.006897496,0.00008053883,0.00006732335,0.0009984114,0.0002224354,0.0002867221,0.0002924269,0.0002429605,0.00001166897],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007222254,"about_ca_system_score_gemma":0.0004606675,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000146199,"about_ca_topic_score_gemma":0.0001314897,"domain_scores_codex":[0.996405,0.0004065568,0.0007405705,0.0003194621,0.001882951,0.0002454806],"domain_scores_gemma":[0.9956284,0.001286029,0.0001148612,0.0005893351,0.002292784,0.00008865156],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001832635,0.0001483993,0.00028064,0.000006633602,0.00001501294,1.23709e-7,0.003069288,0.9389039,0.0002602075,0.0474514,0.006576808,0.003269308],"study_design_scores_gemma":[0.0002766676,0.0001099962,0.00006976983,0.000003858073,0.00001145695,0.000003422734,0.004585482,0.9484888,0.0001823533,0.04244409,0.003741859,0.00008226169],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.29831,0.0003259973,0.6975836,0.001200998,0.0000542272,0.001117483,0.0003459121,0.00002157282,0.001040265],"genre_scores_gemma":[0.8488093,0.0004312975,0.1460033,0.00001794831,0.00002490464,0.0005673596,0.00004831249,0.00001544288,0.004082125],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5515803,"threshold_uncertainty_score":0.9995377,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2571166057204682,"score_gpt":0.4669938152252159,"score_spread":0.2098772095047476,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}