{"id":"W3125078667","doi":"10.2139/ssrn.3288067","title":"A Neural Network Approach to Understanding Implied Volatility Movements","year":2018,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Implied volatility; Volatility (finance); Volatility smile; Econometrics; Moneyness; Artificial neural network; Forward volatility; Index (typography); Economics; Volatility risk premium; Volatility swap; Financial economics; Computer science; Artificial intelligence","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":[0.0004083992,0.0003456284,0.0002875819,0.0005180481,0.0002146727,0.0008392627,0.0004786793,0.0008000641,0.001827272],"category_scores_gemma":[0.002516723,0.0002629524,0.0002674675,0.0003892733,0.0002341532,0.001333134,0.0003532335,0.0009471033,0.0001724959],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003775547,"about_ca_system_score_gemma":0.0003631843,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00500004,"about_ca_topic_score_gemma":0.004229362,"domain_scores_codex":[0.9999152,0.0000222369,0.000007395287,0.00002224598,0.00002112245,0.00001179603],"domain_scores_gemma":[0.9995775,0.0002762547,0.0000476197,0.00002383669,0.00006079533,0.00001397139],"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":[0.00005496272,0.00006355917,0.002889066,0.00003926012,0.00005866205,0.0001081226,0.00004627372,0.8864493,0.004906701,0.03739518,0.0006061068,0.06738281],"study_design_scores_gemma":[9.387294e-7,0.000002487624,0.0001802054,0.000001312259,0.000002265264,0.000003012678,0.000001479846,0.9959568,0.0001153159,0.003684053,0.00005076206,0.000001263947],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1273266,0.0005767049,0.8653855,0.0006493343,0.00007962069,0.00002576417,0.0001653771,0.0002130425,0.005578071],"genre_scores_gemma":[0.9411926,0.0004447874,0.05432036,0.00006217605,0.00008507547,0.00003335695,0.0001253131,0.00002314203,0.003713252],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00500004,"threshold_uncertainty_score":0.009941876,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1592453845000946,"score_gpt":0.3938872030836005,"score_spread":0.2346418185835059,"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."}}