{"id":"W2044121999","doi":"10.1111/j.1467-9892.2006.00496.x","title":"Integer‐Valued GARCH Process","year":2006,"lang":"en","type":"article","venue":"Journal of Time Series Analysis","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":500,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Université du Québec à Montréal","funders":"","keywords":"Autoregressive conditional heteroskedasticity; Mathematics; Autoregressive model; Heteroscedasticity; Integer (computer science); Series (stratigraphy); Applied mathematics; STAR model; Econometrics; Statistics; Time series; Autoregressive integrated moving average; Volatility (finance); Computer science","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":[],"consensus_categories":[],"category_scores_codex":[0.0008013186,0.0001174563,0.0006832599,0.0006603675,0.0000936943,0.00007765984,0.0002348152,0.00007387009,0.0008083443],"category_scores_gemma":[0.0001213791,0.000115353,0.0005348058,0.000992667,0.00004426638,0.0004420799,0.00002417399,0.0001834421,0.0001140723],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006676898,"about_ca_system_score_gemma":0.00003303025,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003534414,"about_ca_topic_score_gemma":0.00006925566,"domain_scores_codex":[0.9984589,0.00001459673,0.001077937,0.0001691537,0.00008057315,0.0001988551],"domain_scores_gemma":[0.9987155,0.00002260471,0.00082125,0.0001812519,0.0002051146,0.00005424105],"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":[0.0006287541,0.0006787851,0.7402171,0.0001314834,0.003706315,0.00006793133,0.001768822,0.1825157,0.0002380365,0.06205995,0.005614488,0.002372628],"study_design_scores_gemma":[0.001716369,0.0007590882,0.1486567,0.00008476924,0.001722012,0.00006410215,0.000653203,0.4752737,0.001016674,0.3256404,0.0431192,0.001293686],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9194698,0.002672099,0.06690522,0.0007088286,0.0001415312,0.00005789022,0.00004243651,0.00001585362,0.009986322],"genre_scores_gemma":[0.9941117,0.00009390503,0.002329829,0.00003211154,0.0002451682,0.000001054259,0.000007367665,0.00001230447,0.003166517],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5915604,"threshold_uncertainty_score":0.8850803,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01246459364899952,"score_gpt":0.2228178056190493,"score_spread":0.2103532119700497,"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."}}