{"id":"W2736245285","doi":"10.5430/ijfr.v8n3p154","title":"Equity Return Modeling and Prediction Using Hybrid ARIMA-GARCH Model","year":2017,"lang":"en","type":"article","venue":"International Journal of Financial Research","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Autoregressive integrated moving average; Autoregressive conditional heteroskedasticity; Akaike information criterion; Econometrics; Heteroscedasticity; Autoregressive model; Volatility clustering; Volatility (finance); Univariate; Time series; Computer science; Economics; Mathematics; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001155101,0.0006860548,0.0008746518,0.0008498844,0.0003007734,0.001005101,0.001491936,0.001006652,0.001121552],"category_scores_gemma":[0.002091634,0.0003418594,0.001056447,0.0009745873,0.0002289132,0.001292689,0.0004343491,0.0009721947,0.0003798098],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003866726,"about_ca_system_score_gemma":0.0005343609,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0104694,"about_ca_topic_score_gemma":0.005641788,"domain_scores_codex":[0.999453,0.0001454618,0.0000357905,0.0001454593,0.0001559187,0.00006432676],"domain_scores_gemma":[0.9994813,0.0002798362,0.00007342058,0.00004601835,0.000103996,0.0000155368],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000648799,0.0000642037,0.006306808,0.00004786527,0.0001376471,0.0001541416,0.00005577996,0.9317579,0.002292923,0.009787005,0.0008772617,0.04845358],"study_design_scores_gemma":[0.000002502607,0.00001149968,0.0005098264,0.000001750267,0.000009165538,0.0000142159,0.000002993327,0.9974585,0.000214281,0.001561222,0.000206841,0.000007122081],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1176927,0.001161321,0.8759448,0.0003292902,0.00009635859,0.00005168251,0.0004358533,0.001307945,0.002980053],"genre_scores_gemma":[0.9116968,0.0007378196,0.08302835,0.0000969557,0.0001026776,0.00008283299,0.0006526946,0.00007059256,0.00353116],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0104694,"threshold_uncertainty_score":0.02081692,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3266219988536908,"score_gpt":0.4314647461251441,"score_spread":0.1048427472714533,"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."}}