{"id":"W2950163193","doi":"10.2139/ssrn.2912029","title":"Vine Copula Models with GLM and Sparsity","year":2017,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Vine copula; Copula (linguistics); Econometrics; Statistics; Mathematics; Computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003628885,0.001485884,0.002567734,0.001382792,0.000737838,0.0030256,0.003020786,0.003393389,0.007614242],"category_scores_gemma":[0.02372161,0.001614456,0.002047907,0.001815308,0.001640906,0.004712197,0.002636141,0.0043971,0.001829962],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008550619,"about_ca_system_score_gemma":0.0009065191,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006972688,"about_ca_topic_score_gemma":0.005783165,"domain_scores_codex":[0.9983938,0.0008625981,0.00006228917,0.00028983,0.0001727341,0.000218823],"domain_scores_gemma":[0.9915876,0.005236048,0.000996999,0.001123841,0.0006163701,0.0004391905],"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":[0.0001052722,0.00008787342,0.001296589,0.00008845332,0.0002211605,0.000264527,0.0001491551,0.6345242,0.0005182906,0.3377444,0.008243369,0.01675669],"study_design_scores_gemma":[0.000009994701,0.00001023062,0.0001546042,0.00000843233,0.000009756825,0.00003032793,0.000009927121,0.9322065,0.00004528378,0.0667458,0.000756066,0.00001317937],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03706976,0.001094621,0.9512209,0.001820306,0.0002310009,0.00006895362,0.0008509239,0.0005355107,0.00710801],"genre_scores_gemma":[0.8200592,0.001773895,0.1226255,0.0009087546,0.001002227,0.0003721052,0.002092172,0.0008755985,0.05029048],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007614242,"threshold_uncertainty_score":0.02547222,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0316615518911651,"score_gpt":0.2171925471003705,"score_spread":0.1855309952092054,"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."}}