{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008482034,0.001213694,0.003248657,0.003438689,0.00212701,0.004197509,0.004261779,0.003489639,0.004865512],"category_scores_gemma":[0.03166985,0.002171976,0.00273234,0.003258396,0.002316264,0.004838117,0.002568413,0.003873786,0.001308167],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002392277,"about_ca_system_score_gemma":0.001353552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006480245,"about_ca_topic_score_gemma":0.00807003,"domain_scores_codex":[0.9955181,0.002307527,0.00021905,0.0009525702,0.000679566,0.0003231864],"domain_scores_gemma":[0.9823987,0.01161824,0.001145802,0.002928356,0.001453457,0.0004554085],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002755747,0.0001564675,0.003930864,0.0001953778,0.0003945096,0.0001401479,0.000419149,0.621542,0.001113551,0.2933479,0.004152086,0.07433238],"study_design_scores_gemma":[0.00001587543,0.00001352142,0.0008781871,0.0000178036,0.00003799554,0.00003177478,0.00003348813,0.8358786,0.0002434593,0.1619277,0.0008848056,0.00003674992],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04158025,0.001144117,0.9538916,0.0005505356,0.00009954191,0.00005719336,0.0002643034,0.0004318262,0.0019807],"genre_scores_gemma":[0.6225905,0.001876155,0.3607835,0.0002775612,0.0004426916,0.0002669954,0.001736173,0.0004762945,0.01155017],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008482034,"threshold_uncertainty_score":0.04485786,"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."}}