{"id":"W2771241241","doi":"10.1016/j.jmva.2019.03.007","title":"Semi-Parametric Copula-Based Models Under Non-Stationarity","year":2018,"lang":"en","type":"article","venue":"Journal of Multivariate Analysis","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke; HEC Montréal; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Copula (linguistics); Estimator; Mathematics; Quantile; Parametric statistics; Bootstrapping (finance); Conditional probability distribution; Asymptotic distribution; Econometrics; Marginal distribution; Monte Carlo method; Statistics; Applied mathematics; Random variable","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.00145089,0.0001652998,0.0007760929,0.001593463,0.0001664524,0.00008050242,0.0002837752,0.0001440952,0.0003550584],"category_scores_gemma":[0.0004070117,0.0001673509,0.000613499,0.002204143,0.00006435016,0.0004578158,0.00003130282,0.0002575919,0.00006699154],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001773496,"about_ca_system_score_gemma":0.0001023129,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001142282,"about_ca_topic_score_gemma":0.00009224508,"domain_scores_codex":[0.9979869,0.0000379645,0.001300839,0.0002832571,0.0001315113,0.0002595038],"domain_scores_gemma":[0.9976742,0.0001518092,0.001230079,0.0003014671,0.0004826057,0.0001597959],"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":[0.0001911745,0.0004516875,0.0807976,0.00001942958,0.001729683,0.000008194856,0.0007095661,0.8893762,0.00006973108,0.02532629,0.0003037541,0.001016663],"study_design_scores_gemma":[0.0007246435,0.00009924151,0.06328004,0.00001082196,0.000266512,9.231333e-7,0.00003853721,0.8957345,0.0000707039,0.03922247,0.000378132,0.0001734964],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3413133,0.0002737222,0.6568683,0.0002494091,0.0001797375,0.00004897179,0.00003543064,0.000007135638,0.001023935],"genre_scores_gemma":[0.9806495,0.00005576077,0.01867077,0.0002606541,0.0002202047,0.000001625193,0.0000105707,0.00001580671,0.0001150734],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6393362,"threshold_uncertainty_score":0.682437,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05635661405748688,"score_gpt":0.2826765760470187,"score_spread":0.2263199619895318,"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."}}