{"id":"W4315589151","doi":"10.48550/arxiv.2301.03517","title":"Diversification quotients based on VaR and ES","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Probability and Risk Models","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; Natural Sciences and Engineering Research Council of Canada; Nankai University","keywords":"Diversification (marketing strategy); Value at risk; Econometrics; Risk measure; Portfolio; Multivariate statistics; Axiom; Mathematics; Portfolio optimization; Model risk; Economics; Actuarial science; Financial economics; Risk management; Mathematical economics; Statistics; Business; Finance","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003726625,0.0007911364,0.0007516841,0.002544285,0.0003926978,0.001972531,0.000646907,0.0006864389,0.001923402],"category_scores_gemma":[0.01415556,0.0002262052,0.0006637925,0.001188153,0.002344465,0.004304977,0.002641968,0.001278645,0.0001825016],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009029907,"about_ca_system_score_gemma":0.0003798836,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004577734,"about_ca_topic_score_gemma":0.0002097925,"domain_scores_codex":[0.998315,0.000722575,0.0001181111,0.0002798292,0.0004304869,0.0001341774],"domain_scores_gemma":[0.9947277,0.002775372,0.0009119753,0.0005494284,0.000566887,0.0004686297],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00005876381,0.00002094357,0.00392951,0.00006729816,0.00004846547,0.0000917493,0.0001638299,0.05243159,0.002658207,0.915703,0.000552875,0.0242737],"study_design_scores_gemma":[0.00001105027,0.00008101207,0.002077968,0.00004223729,0.00001952976,0.000163179,0.00006983345,0.2931321,0.0009044435,0.7009608,0.002505618,0.00003218881],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2782178,0.002732224,0.7003345,0.0006978866,0.0001456449,0.00005304572,0.0002687746,0.0001511164,0.01739909],"genre_scores_gemma":[0.9667447,0.0008174471,0.03002415,0.0000980836,0.0001376421,0.00004961433,0.0001412105,0.00004207542,0.001945133],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003726625,"threshold_uncertainty_score":0.01970857,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3695634720671853,"score_gpt":0.2746498981239177,"score_spread":0.09491357394326755,"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."}}