{"id":"W3122155936","doi":"","title":"Forecasting Realized Volatility: A Bayesian Model Averaging Approach","year":2008,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Econometrics; Realized variance; Volatility (finance); Autoregressive model; Autoregressive conditional heteroskedasticity; Forward volatility; Stochastic volatility; Bayesian probability; Economics; SABR volatility model; Implied volatility; Computer science; Mathematics; Statistics","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":[0.004319454,0.001080846,0.002596101,0.001396674,0.000599859,0.001607146,0.002388818,0.001335246,0.001513977],"category_scores_gemma":[0.01266123,0.001005744,0.001440898,0.001546918,0.000630771,0.002459944,0.0009647484,0.001699695,0.0003663078],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00107318,"about_ca_system_score_gemma":0.001587654,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01932067,"about_ca_topic_score_gemma":0.01607866,"domain_scores_codex":[0.9982033,0.0008557166,0.0001192315,0.0002775794,0.0004369712,0.000107177],"domain_scores_gemma":[0.9961672,0.002715638,0.0003460547,0.0002450151,0.0004450563,0.00008091663],"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":[0.00003655412,0.00003947971,0.00112602,0.00005257557,0.0002205816,0.00004074909,0.00006142464,0.9157013,0.0004642353,0.02582434,0.001274889,0.05515791],"study_design_scores_gemma":[0.000005080653,0.000007734137,0.0001741949,0.000005012572,0.00001554668,0.000006621096,0.000003139879,0.9824879,0.00008715654,0.01692286,0.0002755591,0.000009295439],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01344948,0.0004917328,0.9842314,0.000337703,0.00004167275,0.00002378988,0.0001265177,0.0003224075,0.0009753102],"genre_scores_gemma":[0.5928025,0.001869881,0.3998252,0.0003384417,0.0004774188,0.0002994075,0.001387445,0.000259974,0.002739756],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01932067,"threshold_uncertainty_score":0.03841645,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1176920328374038,"score_gpt":0.29709073780998,"score_spread":0.1793987049725762,"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."}}