{"id":"W2792554359","doi":"10.1080/14697688.2017.1413245","title":"Learning minimum variance discrete hedging directly from the market","year":2018,"lang":"en","type":"article","venue":"Quantitative Finance","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hedge; Valuation of options; Econometrics; Call option; Black–Scholes model; Stochastic volatility; Volatility (finance); Replicating portfolio; Economics; Model risk; Variance (accounting); Variance swap; Sensitivity (control systems); Minimum-variance unbiased estimator; Risk management; Mathematics; Finance; Statistics; SABR volatility model; Portfolio optimization; Portfolio; Mean squared error","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00129786,0.0006484832,0.001063,0.0006262285,0.000150678,0.0007804445,0.0007868148,0.0009364697,0.00109085],"category_scores_gemma":[0.004130199,0.0004199841,0.0005610227,0.0004754277,0.0003723007,0.001206582,0.000690604,0.001426608,0.0002401838],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002804895,"about_ca_system_score_gemma":0.0004269146,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006800081,"about_ca_topic_score_gemma":0.001129944,"domain_scores_codex":[0.9997248,0.00007490891,0.00002049666,0.00008872591,0.00006891212,0.00002219576],"domain_scores_gemma":[0.9981354,0.001448693,0.0001319086,0.0001394361,0.00009591268,0.00004849247],"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":[0.0002406812,0.0002092147,0.005184039,0.0001345772,0.0001181985,0.0001520281,0.0001019378,0.7586672,0.009091624,0.007247956,0.001660306,0.2171922],"study_design_scores_gemma":[0.000005394735,0.00002209726,0.000432648,0.00000400955,0.000004141828,0.00001627162,0.000004586826,0.996295,0.0008320514,0.002273858,0.000105649,0.000004341328],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1319063,0.0003849573,0.8658341,0.0001754365,0.00002810265,0.00002822163,0.0001183561,0.0006352122,0.0008894305],"genre_scores_gemma":[0.8838544,0.0001985363,0.1138833,0.0001273545,0.00005076216,0.0000465898,0.0005382212,0.00006020101,0.001240403],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00129786,"threshold_uncertainty_score":0.006863832,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1065849687980212,"score_gpt":0.4007524762957942,"score_spread":0.294167507497773,"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."}}