{"id":"W2761413829","doi":"","title":"Portfolio Optimization Using Multivariate t-Copulas with Conditionally Skewed Margins","year":2017,"lang":"en","type":"article","venue":"Review of Economics and Finance","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Copula (linguistics); Portfolio; Weighting; Econometrics; Autoregressive conditional heteroskedasticity; Portfolio optimization; Maximization; Computer science; Utility maximization; Economics; Modern portfolio theory; Multivariate statistics; Mathematical economics; Financial economics; Microeconomics; Machine learning","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.000461944,0.0001632133,0.0006516662,0.00006861952,0.000274186,0.00006455353,0.000214826,0.00007154557,0.00005383804],"category_scores_gemma":[0.0001088808,0.0001768612,0.00009043299,0.00004234099,0.0001190662,0.0004374286,0.00006840724,0.0000851478,0.000008808847],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004477508,"about_ca_system_score_gemma":0.00005680538,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003493475,"about_ca_topic_score_gemma":0.00002315523,"domain_scores_codex":[0.9987019,0.000006773588,0.0007130281,0.0003772338,0.0000162461,0.0001848561],"domain_scores_gemma":[0.9981068,0.0000185809,0.001274496,0.0004986121,0.00006559971,0.00003593608],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004919451,0.00009857948,0.02998522,0.00161361,0.00007352547,0.000003709554,0.00006482788,0.04103327,0.000003297134,0.9225484,0.00008693665,0.004439482],"study_design_scores_gemma":[0.001323903,0.0001159977,0.07643028,0.003557719,0.0000453408,0.00001663166,0.000006463638,0.8565285,0.00002145596,0.02630105,0.03498202,0.0006706177],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8393544,0.09227735,0.05434495,0.0008657067,0.0004291005,0.0009105249,0.000788774,0.00001794295,0.01101128],"genre_scores_gemma":[0.6560376,0.3144937,0.02900325,0.0002052329,0.00006209385,0.00001439072,0.00003506013,0.00002583359,0.0001228908],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8962473,"threshold_uncertainty_score":0.7212189,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04898041534381189,"score_gpt":0.2603068888231291,"score_spread":0.2113264734793172,"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."}}