{"id":"W4417051608","doi":"10.3390/jrfm18120679","title":"Volatility Modelling of the JSE Top40 Index: Assessing the GAS Framework Against GARCH and Hybrid GARCH–XGBoost","year":2025,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Research Foundation Singapore","keywords":"Kurtosis; Skewness; Univariate; Autoregressive conditional heteroskedasticity; Residual; Volatility (finance); Gaussian; Studentized residual; Statistic; Probability density function","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":[0.002023254,0.0001730972,0.0004803581,0.0002342464,0.0004483678,0.0001231314,0.0003415299,0.00009352712,0.000004848112],"category_scores_gemma":[0.0003905121,0.0001270696,0.0001880635,0.0003461541,0.000182032,0.0002179973,0.0002792719,0.0006482847,7.260429e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006159143,"about_ca_system_score_gemma":0.00005089967,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000161207,"about_ca_topic_score_gemma":0.00002076228,"domain_scores_codex":[0.9982721,0.00006471274,0.001029946,0.0002625413,0.0001174141,0.0002532542],"domain_scores_gemma":[0.9985259,0.0002260703,0.0007548987,0.0003442154,0.00009900737,0.00004996767],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001822993,0.0001720882,0.564764,0.0002759113,0.00008816585,0.000009931564,0.001520999,0.01193979,0.000003057178,0.1739135,0.0002638016,0.2468665],"study_design_scores_gemma":[0.0007336977,0.00005242636,0.3545679,0.0004817096,0.00007430587,0.000003050067,0.0003356795,0.1792521,0.00002717024,0.4442323,0.02004111,0.0001984928],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5999592,0.004588956,0.3933254,0.0003196697,0.0003928891,0.0001663623,0.00001869905,0.000003401759,0.001225439],"genre_scores_gemma":[0.9900794,0.006749419,0.002777167,0.0001865066,0.0001236727,0.000003779881,4.971575e-7,0.00000994684,0.00006960211],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3905482,"threshold_uncertainty_score":0.5181749,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02267557229980628,"score_gpt":0.2397948788150349,"score_spread":0.2171193065152286,"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."}}