{"id":"W4318823172","doi":"10.2139/ssrn.4184704","title":"Gamma and Vega Hedging Using Deep Distributional Reinforcement Learning","year":2022,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Vega; Reinforcement; Reinforcement learning; Artificial intelligence; Computer science; Econometrics; Economics; Psychology; Social psychology; Physics","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":[0.001131036,0.0004808206,0.0008681055,0.0004057579,0.000308152,0.000916315,0.0009339031,0.00117486,0.002871884],"category_scores_gemma":[0.004112368,0.0003993868,0.0004312938,0.0003058821,0.0006393931,0.001351746,0.001105944,0.001465411,0.0002130486],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005466585,"about_ca_system_score_gemma":0.0005478168,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002550163,"about_ca_topic_score_gemma":0.002945569,"domain_scores_codex":[0.9998149,0.00006409856,0.00001009074,0.00004103309,0.00003715558,0.00003262511],"domain_scores_gemma":[0.9984635,0.001131652,0.00007939715,0.00009733333,0.0001393131,0.00008887006],"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":[0.0001578882,0.00007548819,0.001278481,0.00002875901,0.00004612714,0.00008760342,0.00004250826,0.9303964,0.001131989,0.0170629,0.000927322,0.04876449],"study_design_scores_gemma":[0.000004157154,0.000009412962,0.00006133844,0.00000201779,0.000002823659,0.000005318085,0.00000204425,0.994752,0.0001038615,0.004996801,0.0000580914,0.000002178886],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3075296,0.001467442,0.6825064,0.0009376375,0.000254169,0.0000413843,0.0001110783,0.0007343297,0.00641791],"genre_scores_gemma":[0.9760376,0.0001282882,0.02052523,0.0001016538,0.00003442153,0.00001701766,0.00005125043,0.00003617395,0.003068392],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002871884,"threshold_uncertainty_score":0.009607375,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01321845827348322,"score_gpt":0.2108814823773114,"score_spread":0.1976630241038282,"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."}}