{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00140589,0.0002386649,0.0003218915,0.0005569426,0.0002794648,0.0001634565,0.001166281,0.0003124218,0.00008333165],"category_scores_gemma":[0.0006964468,0.0002180185,0.0001882273,0.0007065743,0.0002472821,0.0002203752,0.001035099,0.0004103322,0.0007088609],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001346807,"about_ca_system_score_gemma":0.0001292259,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001584693,"about_ca_topic_score_gemma":0.00006868131,"domain_scores_codex":[0.9973073,0.0003225253,0.0002809929,0.001467684,0.0003746858,0.0002468066],"domain_scores_gemma":[0.9969907,0.0009449931,0.000290386,0.001336058,0.0002605412,0.0001773299],"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":[0.0002091965,0.0001976195,0.06247325,0.00004081727,0.00003949737,0.000072854,0.0003223623,0.8791855,0.000008041802,0.05402263,0.001508964,0.001919261],"study_design_scores_gemma":[0.0004160208,0.00006501745,0.03397126,0.00006165126,0.00004926613,2.357415e-7,0.0002237598,0.5558457,0.00003465777,0.4083761,0.0006684959,0.0002878666],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9291613,0.00001525879,0.06733426,0.0004531837,0.000721902,0.0003676646,0.0001192637,0.0001691209,0.001658124],"genre_scores_gemma":[0.9961312,0.00008439182,0.000150131,0.0001025817,0.00003422869,7.742623e-7,0.00002429447,0.0000132293,0.003459155],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3543534,"threshold_uncertainty_score":0.911121,"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."}}