{"id":"W2560261639","doi":"10.1016/j.econmod.2016.11.020","title":"Forecasting the realized range-based volatility using dynamic model averaging approach","year":2016,"lang":"en","type":"article","venue":"Economic Modelling","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":51,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Autoregressive model; Volatility (finance); Econometrics; Range (aeronautics); Realized variance; Stochastic volatility; Constant (computer programming); Economics; Mathematics; Computer science; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002413139,0.000314982,0.0005694411,0.0001727822,0.0003921342,0.0001308795,0.0004638568,0.0001481431,0.0001927146],"category_scores_gemma":[0.00005191577,0.0002520066,0.0002848835,0.00009529221,0.0001127347,0.0003767198,0.0001035246,0.0002119896,0.00002398226],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006918486,"about_ca_system_score_gemma":0.00008893476,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004646368,"about_ca_topic_score_gemma":0.00003952746,"domain_scores_codex":[0.9973891,0.00005212021,0.001077084,0.0008572049,0.00004592569,0.0005785628],"domain_scores_gemma":[0.9981676,0.0002420163,0.0005879885,0.0008573777,0.00003047078,0.0001145332],"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":[0.00006791829,0.00003453667,0.02363526,0.00003880619,0.00004556905,5.218618e-7,0.0001801724,0.9629863,0.00001102667,0.0119363,0.0000172592,0.001046334],"study_design_scores_gemma":[0.0007937101,0.000006637377,0.00006995692,0.00002758157,0.00001101898,0.000002468022,0.00001270069,0.9307277,0.000003146984,0.06780633,0.0002097301,0.0003290779],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3882595,0.000163528,0.6047881,0.0001696486,0.0001605403,0.0002486836,0.0001551827,0.00004373376,0.006011095],"genre_scores_gemma":[0.9678925,0.00003594704,0.03136458,0.0001001079,0.00008165698,0.00003251555,0.0000149111,0.00006161239,0.0004161527],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.579633,"threshold_uncertainty_score":0.9999932,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0973889513008145,"score_gpt":0.2362747701573315,"score_spread":0.138885818856517,"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."}}