{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001049967,0.0001779885,0.0003975768,0.00107668,0.0003752296,0.00138471,0.000534218,0.0008239308,0.00152134],"category_scores_gemma":[0.01138697,0.0002854801,0.0003261841,0.0008813412,0.0009258686,0.001531851,0.000840313,0.000638613,0.0001961871],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005923691,"about_ca_system_score_gemma":0.0002843097,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007604043,"about_ca_topic_score_gemma":0.0005012694,"domain_scores_codex":[0.9993964,0.0001549594,0.00003745485,0.0001805305,0.0001510708,0.00007955603],"domain_scores_gemma":[0.9945413,0.002696769,0.001442492,0.0007784487,0.0003244776,0.0002164064],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0003274739,0.0001520826,0.07462832,0.000182328,0.0002742839,0.0005972717,0.0007916937,0.2737016,0.02401002,0.528298,0.002201224,0.09483563],"study_design_scores_gemma":[0.00004027345,0.00009249021,0.04804605,0.00003566139,0.00006805617,0.0007388238,0.000151818,0.4954197,0.005609238,0.4468184,0.002894961,0.00008443597],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8092098,0.0008378012,0.1811108,0.000617695,0.00004233989,0.00002767167,0.0001777073,0.0001994132,0.007776737],"genre_scores_gemma":[0.9943812,0.0001270116,0.004786544,0.0000251394,0.0000251482,0.000008302879,0.00005311702,0.00001759598,0.0005760159],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00152134,"threshold_uncertainty_score":0.005552828,"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."}}