{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.004448117,0.0006490861,0.001693568,0.00135508,0.0005025813,0.0002677316,0.001155729,0.0009999295,0.00004350918],"category_scores_gemma":[0.001209701,0.0008765279,0.0005363371,0.0003322615,0.0003346768,0.000315636,0.001546874,0.003328865,0.00001635239],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00172424,"about_ca_system_score_gemma":0.0007673033,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001175577,"about_ca_topic_score_gemma":0.0001825522,"domain_scores_codex":[0.993385,0.0001466823,0.002324557,0.002368847,0.0001714064,0.001603477],"domain_scores_gemma":[0.9966443,0.0003164168,0.000729901,0.00184381,0.0001420672,0.0003235375],"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":[0.0003003321,0.0005740547,0.05700804,0.0008134003,0.0001988274,0.00004975707,0.004873909,0.8222775,0.000004946479,0.01518366,0.0001411232,0.09857445],"study_design_scores_gemma":[0.00069642,0.00002967052,0.0006414803,0.0001456187,0.000004578831,0.0000107832,0.0001372622,0.9274736,0.000004143338,0.06735592,0.002800958,0.0006995315],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.472571,0.001310611,0.02285939,0.0002312978,0.0006160789,0.001991593,0.0006131873,0.000170491,0.4996364],"genre_scores_gemma":[0.9651014,0.01093882,0.021348,0.00007964725,0.0003173637,0.0003635138,0.0002047942,0.0001747122,0.001471749],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4981646,"threshold_uncertainty_score":0.9993685,"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."}}