{"id":"W1879440715","doi":"10.1016/j.jeconom.2016.03.003","title":"Modeling covariance breakdowns in multivariate GARCH","year":2016,"lang":"en","type":"article","venue":"Journal of Econometrics","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Covariance; Econometrics; Multivariate statistics; Autoregressive conditional heteroskedasticity; Mathematics; Estimation of covariance matrices; Statistics; Volatility (finance)","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":[0.002344846,0.0001605238,0.0006576907,0.001921033,0.00005143005,0.00005153784,0.0003871491,0.0001445425,0.0001811315],"category_scores_gemma":[0.001606434,0.0001408222,0.0002098366,0.0009845115,0.00003179046,0.0007265587,0.00006246092,0.0002711096,0.0001490475],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003600316,"about_ca_system_score_gemma":0.00007808656,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002593623,"about_ca_topic_score_gemma":0.00004352668,"domain_scores_codex":[0.9974717,0.00002160743,0.001776016,0.0002917542,0.00005995589,0.0003789305],"domain_scores_gemma":[0.9985265,0.0002326653,0.0007126277,0.0002630178,0.0001340233,0.0001311388],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005269839,0.0008207601,0.4923433,0.0001186378,0.0002144357,0.0001067072,0.001645498,0.09061965,0.000419693,0.2883068,0.0004062166,0.1244713],"study_design_scores_gemma":[0.00576177,0.0003801963,0.05071944,0.0002719524,0.00001425174,0.00005582027,0.0000966735,0.6511481,0.0001073951,0.2775914,0.01303929,0.0008137093],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5164553,0.002180907,0.4782208,0.0006127495,0.0006170893,0.0000788338,0.00004417456,0.000007339285,0.001782854],"genre_scores_gemma":[0.9885371,0.001195591,0.009741237,0.00008376892,0.0002222481,0.000001594745,5.673091e-7,0.00002370815,0.0001942239],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5605285,"threshold_uncertainty_score":0.5742561,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2080204260477079,"score_gpt":0.2582492913110416,"score_spread":0.05022886526333376,"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."}}