{"id":"W3121859486","doi":"10.2139/ssrn.691887","title":"Bayesian clustering of many GARCH models","year":2003,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"HEC Montréal; Center for Interuniversity Research and Analysis on Organizations","funders":"","keywords":"Autoregressive conditional heteroskedasticity; Series (stratigraphy); Cluster analysis; Bayesian probability; Mathematics; Bayesian inference; Inference; Cluster (spacecraft); Component (thermodynamics); A priori and a posteriori; Econometrics; Statistics; Computer science; Artificial intelligence; Volatility (finance)","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":[],"consensus_categories":[],"category_scores_codex":[0.002967685,0.0001567627,0.0002368847,0.0001602636,0.0001384505,0.00007079643,0.0007790272,0.00007830875,0.000009718815],"category_scores_gemma":[0.00002989184,0.0001367144,0.0001436361,0.0002837159,0.00003141174,0.0004848374,0.00007990935,0.001141938,0.000003494891],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002418652,"about_ca_system_score_gemma":0.001271484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000130533,"about_ca_topic_score_gemma":0.00004648435,"domain_scores_codex":[0.9970155,0.0002733594,0.0003521527,0.000260382,0.0003068988,0.001791643],"domain_scores_gemma":[0.9991861,0.00004496581,0.0001638599,0.0003972292,0.00009079591,0.0001170325],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000004756545,0.00002712403,0.00001824435,0.000005517902,0.0000345613,0.000003831004,0.0001886037,0.0005506393,0.0004211483,0.9248022,0.00001609585,0.07392726],"study_design_scores_gemma":[0.0003167673,0.0001361559,0.000007097779,0.00001693211,0.000008084366,0.0007889914,0.00005016347,0.1251363,0.0006606135,0.8724934,0.0002472999,0.0001382185],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0007385085,0.002000576,0.9864851,0.0002466585,0.0002119534,0.00007708692,2.820462e-7,0.00002995854,0.01020992],"genre_scores_gemma":[0.667774,0.0008641236,0.3303691,0.00007878368,0.00005555853,0.000002247094,1.139583e-7,0.00001477718,0.0008413388],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6670355,"threshold_uncertainty_score":0.5575051,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01455074928103913,"score_gpt":0.256552905204646,"score_spread":0.2420021559236069,"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."}}