{"id":"W2149590736","doi":"10.1007/s10436-015-0268-y","title":"Optimal investment in multidimensional Markov-modulated affine models","year":2015,"lang":"en","type":"article","venue":"Annals of Finance","topic":"Stochastic processes and financial applications","field":"Economics, Econometrics and Finance","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"Technische Universität München","keywords":"Markov chain; Affine transformation; Stochastic volatility; Portfolio optimization; Mathematical finance; Mathematical optimization; Complement (music); Mathematical economics; Markov model; Computer science; Mathematics; Portfolio; Applied mathematics; Econometrics; Economics; Volatility (finance); Financial economics","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":[0.00034496,0.0001274356,0.0003459948,0.0001555303,0.00002866441,0.000007379517,0.0001780975,0.00008389262,0.00001452139],"category_scores_gemma":[0.0001194723,0.0001504067,0.00005532064,0.0004725998,0.00006417863,0.0002002259,0.00006740899,0.00009401567,0.00008721288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002628335,"about_ca_system_score_gemma":0.00006458674,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003387216,"about_ca_topic_score_gemma":0.00002253414,"domain_scores_codex":[0.9987801,0.000003018987,0.0005829834,0.0003224401,0.00005253837,0.0002589615],"domain_scores_gemma":[0.999246,0.00002482442,0.0002708788,0.0002629377,0.0001264177,0.0000689506],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00005528788,0.0002538131,0.0004158819,0.00001363933,0.000009661208,0.000003360472,0.0003030456,0.02482325,0.00003056124,0.9709136,0.002403021,0.0007748454],"study_design_scores_gemma":[0.001296607,0.0002100469,0.02034281,0.00007300844,0.000002656196,0.000003775442,0.00003765019,0.1540059,0.0006859444,0.7881349,0.03478281,0.0004239649],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8224708,0.008565237,0.144392,0.003104404,0.0002259154,0.0004637467,0.000420388,0.0000397742,0.02031775],"genre_scores_gemma":[0.9816805,0.000204808,0.01700638,0.0005954162,0.00004015402,0.00006265906,0.00002952419,0.00001524658,0.000365356],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1827788,"threshold_uncertainty_score":0.6133408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1109376003198289,"score_gpt":0.280377428319755,"score_spread":0.1694398279999262,"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."}}