{"id":"W3124138303","doi":"","title":"Bayesian Semiparametric Stochastic Volatility Modeling","year":2008,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Stochastic volatility; Markov chain Monte Carlo; Econometrics; Kurtosis; Bayesian probability; Semiparametric model; Parametric statistics; Volatility (finance); Skewness; Nonparametric statistics; Computer science; Bayesian inference; Posterior probability; Semiparametric regression; Mathematics; Statistics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.006865569,0.0004419912,0.0007974302,0.00173541,0.0009678577,0.0003306745,0.001668383,0.000755989,0.0001087366],"category_scores_gemma":[0.001714026,0.0005428538,0.000411347,0.0009007093,0.001120258,0.0002252191,0.001336996,0.002716429,0.00002268048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002265599,"about_ca_system_score_gemma":0.001342544,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005867987,"about_ca_topic_score_gemma":0.007362853,"domain_scores_codex":[0.9934223,0.001176434,0.001056272,0.001563004,0.001114471,0.00166749],"domain_scores_gemma":[0.9967018,0.0007160112,0.0002818574,0.001557674,0.0003236179,0.0004190902],"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":[0.00009968501,0.0004567894,0.04886676,0.0002608709,0.0002485871,0.00007353345,0.006329138,0.7675148,0.000001206798,0.001878149,0.0001480176,0.1741225],"study_design_scores_gemma":[0.0004784639,0.00004544708,0.008345042,0.0002076651,0.00002536621,0.00000184747,0.003296662,0.967277,0.000001247865,0.01436196,0.005074092,0.0008851899],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7142807,0.0004721673,0.001456919,0.0003606197,0.001358253,0.002756171,0.0000757275,0.0002036587,0.2790358],"genre_scores_gemma":[0.9798347,0.01732705,0.0008760015,0.00005676672,0.0004747569,0.0003231409,0.00003550134,0.0000700212,0.001002098],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2780337,"threshold_uncertainty_score":0.9997023,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05628013914585686,"score_gpt":0.3558208701496067,"score_spread":0.2995407310037498,"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."}}