{"id":"W3123568641","doi":"","title":"Is Volatility Clustering of Asset Returns Asymmetric","year":2014,"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":"University of Waterloo; Toronto Metropolitan University","funders":"","keywords":"Volatility clustering; Stylized fact; Volatility (finance); Econometrics; Cluster analysis; Forward volatility; Volatility swap; Economics; Foreign exchange; Financial economics; Univariate; Implied volatility; Stock (firearms); Monetary economics; Autoregressive conditional heteroskedasticity; Mathematics; Statistics; Multivariate statistics; Geography","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004596226,0.0004175719,0.001869909,0.002005456,0.0001347367,0.0001922116,0.001124363,0.0006115867,0.001010273],"category_scores_gemma":[0.0007413314,0.0005249736,0.0006681569,0.0006131084,0.0002327561,0.0001333835,0.001809217,0.001427267,0.00007101528],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000860306,"about_ca_system_score_gemma":0.000148361,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002467171,"about_ca_topic_score_gemma":0.001177719,"domain_scores_codex":[0.9950341,0.0001617535,0.00235954,0.001462124,0.0001409576,0.0008415112],"domain_scores_gemma":[0.9958092,0.0004381444,0.001201574,0.002175266,0.0001650089,0.000210831],"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":[0.0003379546,0.0009357975,0.7639698,0.004923312,0.002505756,0.00003180772,0.002945462,0.03362064,0.00003491971,0.02579591,0.001797317,0.1631013],"study_design_scores_gemma":[0.0009209585,0.0001856556,0.08671902,0.0003218796,0.00002351957,0.000006075028,0.0004022241,0.7679806,0.00004604306,0.02493772,0.1173614,0.001094881],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5450813,0.002106902,0.0005070216,0.0006247652,0.001236232,0.001250937,0.001559844,0.00006318777,0.4475698],"genre_scores_gemma":[0.9924727,0.002830408,0.000854913,0.00005382133,0.0002470349,0.00008572141,0.00008627476,0.0000780932,0.003290996],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7343599,"threshold_uncertainty_score":0.999903,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06015897997833092,"score_gpt":0.3002151179772685,"score_spread":0.2400561379989376,"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."}}