{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.02993898,0.0002495822,0.0003848908,0.0003093039,0.0009175928,0.0004275654,0.001281749,0.00009744544,0.0001333098],"category_scores_gemma":[0.003117564,0.0001890341,0.0001939635,0.001658938,0.0001308794,0.0003123111,0.0002947436,0.001461957,0.00007448404],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001949911,"about_ca_system_score_gemma":0.001006842,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002341293,"about_ca_topic_score_gemma":0.0002465246,"domain_scores_codex":[0.9925095,0.0008377885,0.000852751,0.0006376729,0.001570619,0.003591701],"domain_scores_gemma":[0.997525,0.0008970106,0.0004077911,0.0006124056,0.0002496114,0.0003082026],"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":[0.001796063,0.0002909219,0.1155642,0.000005838731,0.0004826096,0.000005464852,0.002457787,0.002701936,0.0004820733,0.463863,0.01441067,0.3979395],"study_design_scores_gemma":[0.0005074052,0.0004678919,0.009121847,0.000008439503,0.00001469285,0.000212808,0.002055299,0.02407164,0.0000140574,0.961947,0.001351924,0.0002269566],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2387054,0.0001192181,0.7295285,0.0003602338,0.0009421075,0.0002561489,0.000001404292,0.00004556886,0.03004132],"genre_scores_gemma":[0.9845786,0.000008454651,0.01141042,0.0004288559,0.001351873,0.000006694119,6.478588e-7,0.00002888233,0.002185564],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7458732,"threshold_uncertainty_score":0.9988819,"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."}}