{"id":"W3110775583","doi":"10.1016/j.jeconom.2025.106040","title":"A multivariate realized GARCH model","year":2025,"lang":"en","type":"article","venue":"Journal of Econometrics","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"Singapore Management University; Austrian Science Fund","keywords":"Parametrization (atmospheric modeling); Covariance matrix; Autoregressive conditional heteroskedasticity; Transformation (genetics); Mathematics; Gaussian; Applied mathematics; Multivariate statistics; Positive definiteness; Multivariate normal distribution; Factor analysis; Matrix (chemical analysis); Logarithm; Distribution (mathematics); Correlation; Series (stratigraphy); Econometrics; Algorithm; Positive-definite matrix; Statistics; Mathematical analysis","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":[0.001589735,0.0005683724,0.001017754,0.0005654616,0.0004354257,0.001724938,0.001358272,0.001795613,0.004717926],"category_scores_gemma":[0.005673369,0.0005121685,0.001049761,0.0009653654,0.0005507678,0.002062369,0.000684457,0.001835063,0.001029364],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006215033,"about_ca_system_score_gemma":0.0009123133,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004822608,"about_ca_topic_score_gemma":0.002701821,"domain_scores_codex":[0.9993902,0.000255969,0.00002507634,0.0001297799,0.000133862,0.00006510744],"domain_scores_gemma":[0.9985275,0.0008475466,0.0001390804,0.000226355,0.0001961706,0.00006340504],"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":[0.0001321618,0.00008691935,0.002685637,0.00006745294,0.0001698662,0.0003198318,0.0001052548,0.6322837,0.002288947,0.3163905,0.004205023,0.04126467],"study_design_scores_gemma":[0.00001753997,0.00001712477,0.0004728102,0.000004473497,0.00002720878,0.00005123563,0.000005525253,0.9575036,0.0001774985,0.04054891,0.001156076,0.0000180002],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08351582,0.0009870769,0.900631,0.001820918,0.0003389215,0.0000361266,0.0005506252,0.001108441,0.01101097],"genre_scores_gemma":[0.908977,0.000917288,0.06968348,0.0002063451,0.0004258536,0.00005647788,0.0005805882,0.0001833599,0.01896955],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004822608,"threshold_uncertainty_score":0.01578301,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2145342295346609,"score_gpt":0.2859493541613336,"score_spread":0.07141512462667265,"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."}}