{"id":"W2014914677","doi":"10.1016/j.pacfin.2014.07.005","title":"Risk contributions of trading and non-trading hours: Evidence from Chinese commodity futures markets","year":2014,"lang":"en","type":"article","venue":"Pacific-Basin Finance Journal","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Futures contract; Financial economics; Open outcry; Algorithmic trading; Commodity; High-frequency trading; Business; Commodity pool; Forward market; Futures market; Electronic trading; Economics; Monetary economics; Alternative trading system; Commerce; Finance; Market liquidity","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":[0.001033266,0.0002870623,0.0003123096,0.001064148,0.0005302747,0.001165344,0.0006179947,0.0004830026,0.00272743],"category_scores_gemma":[0.005626799,0.000237119,0.0004583747,0.001186957,0.0007583314,0.001545105,0.0007464176,0.0006496955,0.0002203665],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006110747,"about_ca_system_score_gemma":0.0004739964,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02426088,"about_ca_topic_score_gemma":0.01901112,"domain_scores_codex":[0.9997645,0.00004312096,0.00002112018,0.00004152664,0.00007730974,0.00005238338],"domain_scores_gemma":[0.9951752,0.002636411,0.001023722,0.0002844555,0.000402999,0.0004771181],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001602046,0.0002634702,0.9484386,0.000129201,0.0003077783,0.001074625,0.001506826,0.006449963,0.003926486,0.006101588,0.001033565,0.02916581],"study_design_scores_gemma":[0.00003689003,0.00006953063,0.9886492,0.000009719759,0.0001196052,0.00008892424,0.0004880146,0.00696401,0.0005953106,0.002152566,0.0008018676,0.00002420188],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9981434,0.0003395553,0.0001145422,0.0001026517,0.000004523142,0.000003633878,0.0001295712,0.000002905661,0.001159184],"genre_scores_gemma":[0.9993182,0.0001971671,0.00002793518,0.000008857423,0.00001579305,0.000001329553,0.0001743098,0.000001646119,0.0002547114],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02426088,"threshold_uncertainty_score":0.04823935,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01466900381116609,"score_gpt":0.2339051818670974,"score_spread":0.2192361780559313,"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."}}