{"id":"W4387543391","doi":"10.35219/eai15840409338","title":"Quantifying Long-Term Volatility for Developed Stock Markets: An Empirical Case Study Using PGARCH Model on Toronto Stock Exchange (TSX)","year":2023,"lang":"en","type":"article","venue":"Annals of Dunarea de Jos University of Galati Fascicle I Economics and Applied Informatics","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Volatility (finance); Autoregressive conditional heteroskedasticity; Econometrics; Economics; Stock exchange; Stock (firearms); Stock market index; Index (typography); Stock market; Financial economics; Finance; Computer science; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001340545,0.0002235908,0.0006558375,0.0002506975,0.0003527061,0.00004859871,0.000257383,0.0001664982,0.00001650669],"category_scores_gemma":[0.00003501966,0.0002994307,0.0001277742,0.0001596094,0.00008828934,0.0006690659,0.0001899917,0.0001238257,0.000002516246],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001320859,"about_ca_system_score_gemma":0.0001064342,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008890493,"about_ca_topic_score_gemma":0.001270903,"domain_scores_codex":[0.9983338,0.0000180282,0.0008927943,0.0003026636,0.00005380602,0.0003989683],"domain_scores_gemma":[0.998563,0.0001285075,0.0006420105,0.0003874587,0.0001138581,0.0001651647],"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":[0.001786667,0.001333679,0.739172,0.001426287,0.0004622315,0.00002164257,0.1081503,0.09740612,0.00003090735,0.009637362,0.0002727011,0.04030005],"study_design_scores_gemma":[0.001077623,0.0002541106,0.05852569,0.00002404352,0.00002049301,0.000005092071,0.008023763,0.9308012,0.00002851453,0.0008867256,0.00006724423,0.0002855541],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9791743,0.00004477812,0.01900643,0.00003867903,0.00003696799,0.0007945978,0.0003899417,0.0000311152,0.0004831463],"genre_scores_gemma":[0.993748,0.0003318668,0.005760513,0.00005826862,0.00001772178,0.000003238982,0.00003321435,0.00002215453,0.00002498592],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.833395,"threshold_uncertainty_score":0.9999458,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2687515929795075,"score_gpt":0.3498566042738068,"score_spread":0.08110501129429931,"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."}}