{"id":"W2020439483","doi":"10.1198/073500102288618513","title":"Conditional Jump Dynamics in Stock Market Returns","year":2002,"lang":"en","type":"article","venue":"Journal of Business and Economic Statistics","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":363,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of Alberta","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Jump; Econometrics; Autoregressive conditional heteroskedasticity; Autoregressive model; Conditional probability distribution; Volatility (finance); Heteroscedasticity; Mathematics; Stock (firearms); Conditional variance; Conditional expectation; Economics","routes":{"ca_aff":true,"ca_fund":true,"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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004361369,0.0001196861,0.0004476505,0.0002754665,0.00005700442,0.00006371897,0.0001099631,0.00008315315,0.0009999552],"category_scores_gemma":[0.000170216,0.0001385989,0.00004515214,0.00008924995,0.00005876873,0.0002909472,0.00002746162,0.0001869248,0.00003696729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002982094,"about_ca_system_score_gemma":0.00002824325,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001859248,"about_ca_topic_score_gemma":0.0003882587,"domain_scores_codex":[0.9986402,0.000009749403,0.0009756527,0.0001619725,0.00002774963,0.000184716],"domain_scores_gemma":[0.9990453,0.0001111102,0.0006036741,0.0001009444,0.00006894807,0.00007009789],"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":[0.0000976187,0.000174559,0.5872805,0.0001273791,0.00006372158,0.00005903778,0.0004020957,0.0039323,8.791048e-7,0.3792543,0.02063952,0.007968087],"study_design_scores_gemma":[0.0007504906,0.00004239568,0.2895509,0.00002566514,0.000007132017,0.00004654576,0.00005489915,0.6181607,2.545723e-7,0.08756286,0.003633705,0.0001643998],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7793854,0.003323858,0.2026463,0.001099199,0.001455349,0.0001495496,0.002479,0.000008522462,0.009452896],"genre_scores_gemma":[0.9911195,0.002231099,0.005950704,0.00007340546,0.0001842147,0.000001559898,0.00001550654,0.00001600967,0.0004079799],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6142284,"threshold_uncertainty_score":0.9999133,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03366994499778126,"score_gpt":0.2209168363121451,"score_spread":0.1872468913143639,"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."}}