{"id":"W2082102138","doi":"10.1016/j.econmod.2014.04.010","title":"Modeling conditional covariance for mixed-asset portfolios","year":2014,"lang":"en","type":"article","venue":"Economic Modelling","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Estimator; Econometrics; Covariance; Conditional variance; Portfolio; Economics; Stock (firearms); Portfolio optimization; Range (aeronautics); Mathematics; Financial economics; Statistics; Autoregressive conditional heteroskedasticity; Volatility (finance); Engineering","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":[0.004487905,0.0009915809,0.001469659,0.001176201,0.0004466424,0.002330434,0.001899377,0.002062877,0.0022116],"category_scores_gemma":[0.0173919,0.001435918,0.00168394,0.001116199,0.0008576367,0.003332617,0.001388076,0.001946986,0.0003447781],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001162991,"about_ca_system_score_gemma":0.001482991,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01296766,"about_ca_topic_score_gemma":0.01393992,"domain_scores_codex":[0.998963,0.0005118297,0.0000614585,0.0001738758,0.0001700172,0.0001197777],"domain_scores_gemma":[0.9917053,0.006590895,0.0006562317,0.0003888793,0.0004315859,0.0002270984],"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":[0.00004252957,0.0000382816,0.001829995,0.00003879906,0.000167818,0.00005996733,0.00004242586,0.921183,0.0004584262,0.06823167,0.0004670955,0.007439785],"study_design_scores_gemma":[0.00000321257,0.000003082803,0.0001177072,0.000002958593,0.000007623602,0.000007231213,0.000002022419,0.9878335,0.00006840107,0.01186336,0.00008662983,0.000004353781],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04932272,0.0004348167,0.9482362,0.0003357081,0.00004650584,0.00002074707,0.0001534436,0.0002283829,0.001221571],"genre_scores_gemma":[0.8400497,0.001048409,0.1482862,0.0002031481,0.0001882748,0.0001644413,0.0008661376,0.0003359815,0.00885777],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01296766,"threshold_uncertainty_score":0.02578437,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05153150566925403,"score_gpt":0.2327225737455933,"score_spread":0.1811910680763393,"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."}}