{"id":"W2948259043","doi":"10.1002/sta4.232","title":"Stochastic volatility generated by product autoregressive models","year":2019,"lang":"en","type":"article","venue":"Stat","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"University Grants Commission","keywords":"Autoregressive model; Stochastic volatility; STAR model; Econometrics; Volatility (finance); Markov chain; Nonlinear autoregressive exogenous model; SETAR; Mathematics; Hidden Markov model; Computer science; Applied mathematics; Autoregressive integrated moving average; Statistics; Time series; Artificial intelligence","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":[0.0003320989,0.0001703035,0.0003631688,0.0000820022,0.00008763032,0.00005029662,0.0001740126,0.00007848805,0.0003247585],"category_scores_gemma":[0.00008834365,0.0001879273,0.00007736116,0.0001453335,0.00003708846,0.0003519761,0.00005121325,0.0001802631,0.0005744314],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009806159,"about_ca_system_score_gemma":0.00003878309,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004593419,"about_ca_topic_score_gemma":0.00002200079,"domain_scores_codex":[0.9985439,0.00001387031,0.0004675351,0.0005842355,0.00005012988,0.000340321],"domain_scores_gemma":[0.9991859,0.00002771406,0.0001978737,0.0004469422,0.00006563216,0.00007599453],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005314774,0.00111055,0.193557,0.0004600075,0.0003808958,0.00001045201,0.01185795,0.3109242,0.003421566,0.4033095,0.05207713,0.02235929],"study_design_scores_gemma":[0.0003593852,0.00003814684,0.001574914,0.00001131675,0.000003087766,4.806791e-7,0.00002308831,0.9340349,0.00009618377,0.0609439,0.002664303,0.000250247],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8609029,0.003275269,0.1303509,0.0001588606,0.0005837542,0.0004064971,0.0005318302,0.00007314111,0.00371679],"genre_scores_gemma":[0.9960351,0.00002562375,0.000644116,0.00007959651,0.00007058016,0.0000170207,0.00007444277,0.00002606292,0.003027435],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6231108,"threshold_uncertainty_score":0.7663455,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02891016496328693,"score_gpt":0.2160526202988636,"score_spread":0.1871424553355766,"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."}}