{"id":"W4407098767","doi":"10.1016/j.najef.2025.102376","title":"Multivariate Affine GARCH in portfolio optimization. Analytical solutions and applications","year":2025,"lang":"en","type":"article","venue":"The North American Journal of Economics and Finance","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Multivariate statistics; Econometrics; Affine transformation; Portfolio optimization; Portfolio; Autoregressive conditional heteroskedasticity; Economics; Mathematics; Computer science; Financial economics; Statistics; 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":[],"consensus_categories":[],"category_scores_codex":[0.0005652416,0.0001031954,0.0004051878,0.0002525201,0.0001501022,0.00004489857,0.0001649886,0.00002848742,0.000005216299],"category_scores_gemma":[0.00006225255,0.00009859259,0.00005683301,0.0003802188,0.0002427075,0.0001411895,0.00006740238,0.0002072549,0.00000176813],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005296073,"about_ca_system_score_gemma":0.00006525729,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002707996,"about_ca_topic_score_gemma":0.000219257,"domain_scores_codex":[0.9988444,0.00001420927,0.0007359388,0.0001975743,0.00001150933,0.0001963281],"domain_scores_gemma":[0.9991682,0.00009886242,0.0004784922,0.0001716859,0.00004598987,0.00003681665],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007695545,0.00008656505,0.162659,0.000008563788,0.00004298683,0.000001241274,0.0002142078,0.3514521,1.843955e-7,0.4318609,0.00004469746,0.05355264],"study_design_scores_gemma":[0.0005268773,0.00008249523,0.2630226,0.00001707869,0.00001408289,0.000009643487,0.00007904106,0.6989859,3.745468e-7,0.02049704,0.01661782,0.0001470307],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9136016,0.003093998,0.08022922,0.001619403,0.00007685617,0.0001597217,0.00004837431,0.000003276831,0.001167587],"genre_scores_gemma":[0.9820244,0.01447992,0.003132701,0.000170854,0.00006033305,0.00001052613,0.00000207277,0.000007378359,0.0001118523],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4113638,"threshold_uncertainty_score":0.4020489,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02210979765520765,"score_gpt":0.2349374660034994,"score_spread":0.2128276683482917,"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."}}