{"id":"W2161024837","doi":"10.5539/ijef.v2n1p51","title":"Financial Volatility Forecasting by Least Square Support Vector Machine Based on GARCH, EGARCH and GJR Models: Evidence from ASEAN Stock Markets","year":2010,"lang":"en","type":"article","venue":"International Journal of Economics and Finance","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institut de Mathématiques de Toulouse","keywords":"Autoregressive conditional heteroskedasticity; Leverage effect; Volatility (finance); Support vector machine; Econometrics; Stock (firearms); Financial market; Economics; Stock market; Leverage (statistics); Financial economics; Computer science; Finance; Artificial intelligence; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00465498,0.0001962256,0.0003825502,0.0002509289,0.0001277151,0.0003142205,0.0008100596,0.0001040464,0.0001592888],"category_scores_gemma":[0.007642899,0.000169006,0.0001287308,0.00009044932,0.000160432,0.0006929163,0.0001838523,0.000549985,0.000002266579],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006951372,"about_ca_system_score_gemma":0.0002540952,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008770849,"about_ca_topic_score_gemma":0.0001854436,"domain_scores_codex":[0.9977183,0.0001555779,0.0009712085,0.0004632861,0.0004801691,0.0002114544],"domain_scores_gemma":[0.9939188,0.004215523,0.0009343405,0.0002739178,0.000560796,0.00009659155],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002881587,0.0001452897,0.07236281,0.000006584844,0.00004296726,0.00005540224,0.0003341257,0.005787179,0.0002494774,0.001204791,0.002508594,0.9144212],"study_design_scores_gemma":[0.0007488739,0.000210944,0.08546224,0.0001078023,0.000008180934,0.00007525043,0.00001089144,0.8772631,0.0001395404,0.02930836,0.006500703,0.0001640965],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9827665,0.0002464909,0.01339183,0.001270197,0.001357964,0.0001048371,0.0003733568,0.00000387748,0.0004849342],"genre_scores_gemma":[0.9818269,0.0001518291,0.01736012,0.0002195081,0.0002763954,0.000003582097,0.000005859608,0.00001434321,0.0001414798],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9142571,"threshold_uncertainty_score":0.9149817,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1163553042737186,"score_gpt":0.3549224068616514,"score_spread":0.2385671025879328,"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."}}