{"id":"W2324833241","doi":"10.1002/num.21836","title":"Continuous time mean‐variance optimal portfolio allocation under jump diffusion: An numerical impulse control approach","year":2013,"lang":"en","type":"article","venue":"Numerical Methods for Partial Differential Equations","topic":"Stochastic processes and financial applications","field":"Economics, Econometrics and Finance","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Hamilton–Jacobi–Bellman equation; Viscosity solution; Mathematics; Jump diffusion; Monotone polygon; Efficient frontier; Partial differential equation; Mathematical optimization; Portfolio; Applied mathematics; Optimal control; Jump; Mathematical analysis; Economics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001633358,0.0006854609,0.0009716409,0.0006781423,0.0004612491,0.00121551,0.0009192251,0.001770732,0.003151589],"category_scores_gemma":[0.004015271,0.0004472084,0.0008175491,0.0006381852,0.001709912,0.000929485,0.001496328,0.001545948,0.0003056023],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00105971,"about_ca_system_score_gemma":0.001574084,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003837553,"about_ca_topic_score_gemma":0.002540171,"domain_scores_codex":[0.9996524,0.000160534,0.00001552161,0.00003303373,0.0001053043,0.00003324855],"domain_scores_gemma":[0.9987571,0.0008541868,0.0001214029,0.00006115394,0.0001411227,0.0000649738],"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":[0.00002270812,0.00003137019,0.000291966,0.00007140465,0.00002434314,0.00007313192,0.00006211694,0.9097598,0.001350174,0.0782547,0.0004525619,0.009605765],"study_design_scores_gemma":[0.000004756912,0.000004613817,0.00001149517,0.000004316908,0.000001837771,0.000004284723,0.000002464098,0.9951519,0.00008844818,0.004437468,0.0002856597,0.000002679872],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008649451,0.0004058306,0.9850975,0.000460881,0.0000766206,0.00004606011,0.00002649715,0.00007248836,0.005164606],"genre_scores_gemma":[0.5313538,0.0009941001,0.4526355,0.0003612028,0.0001393611,0.0005622697,0.0001197221,0.0001441317,0.01368986],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003837553,"threshold_uncertainty_score":0.01054311,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03712631515068292,"score_gpt":0.3044200689248387,"score_spread":0.2672937537741558,"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."}}