{"id":"W2027158222","doi":"10.1142/s0219024905002883","title":"BAYESIAN MODEL SELECTION VIA FILTERING FOR A CLASS OF MICRO-MOVEMENT MODELS OF ASSET PRICE","year":2005,"lang":"en","type":"article","venue":"International Journal of Theoretical and Applied Finance","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Bayes factor; Stochastic volatility; Model selection; Recursive Bayesian estimation; Computer science; Bayesian programming; Robustness (evolution); Bayesian probability; Mathematical optimization; Bayesian inference; Bayes' theorem; Markov chain; Selection (genetic algorithm); Volatility (finance); Econometrics; Algorithm; Machine learning; Mathematics; Artificial intelligence","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":[0.007642709,0.001020602,0.002272207,0.001717128,0.0009527553,0.002060728,0.002531127,0.002094666,0.001692984],"category_scores_gemma":[0.02984053,0.001156603,0.0023242,0.00109864,0.001902532,0.003293271,0.001786559,0.002459736,0.0003699361],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001582189,"about_ca_system_score_gemma":0.002041384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008488622,"about_ca_topic_score_gemma":0.006984994,"domain_scores_codex":[0.9963897,0.001760865,0.0001390537,0.0007714234,0.0007096682,0.0002292462],"domain_scores_gemma":[0.9845968,0.0126725,0.0009934813,0.0008118481,0.0007100378,0.0002154973],"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":[0.0000709105,0.00008460595,0.00360958,0.0001238337,0.0002354758,0.0002426278,0.0002352114,0.6445697,0.0009772242,0.307502,0.00131704,0.04103179],"study_design_scores_gemma":[0.0000131675,0.00001303058,0.0002557231,0.000009755826,0.00001303976,0.00003216653,0.000008251702,0.9251183,0.0001197411,0.07399304,0.000410832,0.00001292564],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01099343,0.0001504629,0.9879003,0.0002647436,0.00001375658,0.00003381077,0.00004475292,0.0000723141,0.0005263954],"genre_scores_gemma":[0.5544009,0.001233546,0.4386007,0.000377622,0.0002416325,0.0005745298,0.0007338894,0.0001388367,0.003698475],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008488622,"threshold_uncertainty_score":0.04041904,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01552816008407178,"score_gpt":0.2348159609707513,"score_spread":0.2192878008866795,"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."}}