{"id":"W2047726268","doi":"10.1016/j.omega.2015.01.021","title":"Portfolio optimization in hedge funds by OGARCH and Markov Switching Model","year":2015,"lang":"en","type":"article","venue":"Omega","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Natural Science Foundation of China","keywords":"Portfolio; EWMA chart; Sharpe ratio; Hedge fund; Econometrics; Portfolio optimization; Markov chain; Asset (computer security); Post-modern portfolio theory; Mathematics; Sensitivity (control systems); Economics; Replicating portfolio; Actuarial science; Computer science; Statistics; Financial economics; Finance; Process (computing); Engineering","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":[0.003084501,0.0006314279,0.001475694,0.0006953136,0.0003412375,0.001433195,0.0007822958,0.001078697,0.0005860441],"category_scores_gemma":[0.005555715,0.0004865663,0.000942739,0.0007690116,0.000578694,0.001360538,0.000753959,0.0008502861,0.00008650477],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008475803,"about_ca_system_score_gemma":0.001031842,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008724959,"about_ca_topic_score_gemma":0.003888322,"domain_scores_codex":[0.9987206,0.0007419613,0.00005659288,0.0001337407,0.0001999422,0.0001472012],"domain_scores_gemma":[0.9981901,0.001343085,0.000222944,0.00007205219,0.0001117103,0.00006002585],"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":[0.00008911428,0.00002212146,0.001567357,0.00001914821,0.0000690074,0.00003874048,0.00001887968,0.981743,0.0003823662,0.008331931,0.00008516751,0.007633184],"study_design_scores_gemma":[0.00001371046,0.00003123068,0.0003324526,0.000002756546,0.00001251798,0.000007694348,0.000005322937,0.9957697,0.00017048,0.003590827,0.00005766024,0.000005694167],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5413179,0.0013173,0.4535533,0.0003600724,0.0000482219,0.00004523803,0.000106635,0.0001653543,0.003085978],"genre_scores_gemma":[0.981202,0.0003448477,0.01744568,0.00003840408,0.00001789684,0.00003263159,0.00007544459,0.00001106747,0.0008319783],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008724959,"threshold_uncertainty_score":0.01734835,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03445848350230071,"score_gpt":0.2328977707838322,"score_spread":0.1984392872815315,"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."}}