{"id":"W3122446657","doi":"","title":"Trading Frequency and Volatility Clustering","year":2009,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Volatility clustering; Stylized fact; Volatility (finance); Econometrics; Cluster analysis; Economics; Market microstructure; Forward volatility; Realized variance; Volatility smile; Financial market; Financial economics; Mathematics; Autoregressive conditional heteroskedasticity; Statistics; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002712241,0.0003621929,0.00117761,0.00088877,0.0002244374,0.0003680617,0.0005631373,0.0004247342,0.0004734166],"category_scores_gemma":[0.0002465871,0.0004692173,0.0002753103,0.0002085311,0.0001823966,0.0001833758,0.0007830936,0.001292273,0.00001767741],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008302244,"about_ca_system_score_gemma":0.00009097592,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001593481,"about_ca_topic_score_gemma":0.001565328,"domain_scores_codex":[0.9962854,0.00009744355,0.001415819,0.001356218,0.00006991795,0.0007752441],"domain_scores_gemma":[0.9980121,0.0001773014,0.0004439288,0.001100127,0.00004864981,0.0002178408],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001506688,0.0005071133,0.4973313,0.001716585,0.0009937473,0.0001202191,0.003339528,0.005323601,0.00007376049,0.0746751,0.000198166,0.4155703],"study_design_scores_gemma":[0.0008572533,0.0001560611,0.1091128,0.000341994,0.00001531729,0.00002077622,0.0005160283,0.6575508,0.000006587,0.2053199,0.02482202,0.00128049],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6662723,0.003641786,0.0001570732,0.0005207487,0.0004552839,0.0007576004,0.0002382076,0.00006110391,0.3278958],"genre_scores_gemma":[0.9921234,0.004772572,0.001355937,0.00004394119,0.0002413972,0.00007005038,0.00003778418,0.00005213452,0.001302746],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6522272,"threshold_uncertainty_score":0.9997759,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05788892224195517,"score_gpt":0.2853271104419633,"score_spread":0.2274381882000082,"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."}}