{"id":"W4387384456","doi":"10.2139/ssrn.4593086","title":"Mean-Variance Optimization Under Affine GARCH: A Utility-Based Solution","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Affine transformation; Autoregressive conditional heteroskedasticity; Variance (accounting); Mathematics; Mathematical optimization; Econometrics; Computer science; Economics; Pure mathematics; Volatility (finance)","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":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.004888925,0.0003946174,0.0007037277,0.0005494859,0.0003861752,0.0002066564,0.0005813047,0.0005301035,0.0001389031],"category_scores_gemma":[0.0003026711,0.0004881292,0.0004247639,0.0003714457,0.0000674557,0.0002101156,0.0002280398,0.004326771,0.0001721925],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002081082,"about_ca_system_score_gemma":0.002430151,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001013549,"about_ca_topic_score_gemma":0.001875047,"domain_scores_codex":[0.9953933,0.00007717229,0.001239247,0.0008345708,0.000143918,0.002311737],"domain_scores_gemma":[0.9981055,0.00007434081,0.0009599021,0.0005874041,0.0001605811,0.0001122464],"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":[0.0001105093,0.0001302484,0.002167901,0.00007392903,0.0001732261,0.000002441852,0.0001856962,0.6852868,0.000003032598,0.3087808,0.0001606842,0.002924692],"study_design_scores_gemma":[0.0004043375,0.00005872704,0.0004516448,0.00005960628,0.00001795861,0.00000580581,0.00005874442,0.521426,0.0000025094,0.4765498,0.0006836301,0.0002811954],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009566226,0.008811031,0.9761729,0.002663035,0.001411319,0.0003415492,0.0001180193,0.0001481759,0.0007677443],"genre_scores_gemma":[0.981978,0.009710662,0.0054037,0.0001428706,0.0007777399,0.00003643783,0.0001832658,0.0001117091,0.001655622],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9724118,"threshold_uncertainty_score":0.9997571,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0734659631228563,"score_gpt":0.2614027913374646,"score_spread":0.1879368282146083,"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."}}