{"id":"W2798247192","doi":"10.3390/jrfm11020029","title":"Leverage and Volatility Feedback Effects and Conditional Dependence Index: A Nonparametric Study","year":2018,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Econometrics; Nonparametric statistics; Leverage effect; Volatility (finance); Economics; Tail dependence; Index (typography); Financial economics; Mathematics; Autoregressive conditional heteroskedasticity; Statistics; Multivariate statistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001284866,0.0001767705,0.0004790417,0.0004318483,0.0002868129,0.0001022976,0.0001208851,0.00008674584,0.00001379943],"category_scores_gemma":[0.0003773715,0.0001791398,0.00006149848,0.0003104534,0.0001478876,0.0003437318,0.0001567292,0.0002701186,0.000006626381],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004215874,"about_ca_system_score_gemma":0.00001671673,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001855722,"about_ca_topic_score_gemma":0.00008293558,"domain_scores_codex":[0.9985958,0.00004026732,0.0006883385,0.0003445302,0.0001076613,0.0002234573],"domain_scores_gemma":[0.9990065,0.0001311168,0.0004993156,0.0001511956,0.00009137498,0.0001205227],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002015668,0.0002406104,0.9223904,0.00008170335,0.00004263166,0.00003511082,0.001200584,0.0000115494,9.160178e-7,0.01150511,0.0000963908,0.06419345],"study_design_scores_gemma":[0.00169108,0.000689088,0.930231,0.00003395892,0.00004354327,0.0000140174,0.0001441455,0.005151671,0.000003056283,0.05809832,0.003723762,0.0001763158],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9105834,0.002787281,0.08565054,0.00003070145,0.0002961708,0.0003033835,0.00003024058,0.000006312644,0.0003119874],"genre_scores_gemma":[0.9957846,0.002411405,0.001460807,0.00007079687,0.0002141314,0.000005571985,7.889018e-7,0.00001016612,0.00004175894],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0852012,"threshold_uncertainty_score":0.7305111,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01372644978679028,"score_gpt":0.2229711282871586,"score_spread":0.2092446785003683,"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."}}