{"id":"W114269338","doi":"","title":"Volatility in Stock Markets of India and Canada","year":2012,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Volatility (finance); Stylized fact; Economics; Volatility clustering; Volatility swap; Volatility smile; Forward volatility; Financial economics; Stock (firearms); Volatility risk premium; Implied volatility; Stock market; Autoregressive conditional heteroskedasticity; Econometrics; Monetary economics; Geography; Macroeconomics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002260534,0.00008393043,0.0002456357,0.0001096871,0.00005466906,0.000008768653,0.0000975809,0.00005785253,0.00002702005],"category_scores_gemma":[0.0001364829,0.00009463316,0.00003527956,0.0001021779,0.00002038557,0.0002143018,0.00002497146,0.0006986161,0.000001511167],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006563275,"about_ca_system_score_gemma":0.0007265236,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.2793284,"about_ca_topic_score_gemma":0.5424133,"domain_scores_codex":[0.9982682,0.00002016243,0.0004690494,0.0001161678,0.00003882851,0.00108755],"domain_scores_gemma":[0.9995655,0.00003536289,0.0002131101,0.0001008082,0.0000179944,0.00006716703],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002223876,0.00002724663,0.9389815,0.000007000273,0.00001301944,2.111605e-7,0.0001341109,0.000003659049,0.000002565231,0.05675534,0.00001792936,0.004035166],"study_design_scores_gemma":[0.0003682559,0.00003584734,0.8826975,0.000009202441,0.00000268234,0.00001728187,0.0002387585,0.002138191,0.000008636192,0.1132422,0.001123645,0.0001178231],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9838363,0.0134549,0.001475988,0.00008388297,0.000153081,0.00006203137,0.00001684187,0.000001931735,0.0009150209],"genre_scores_gemma":[0.9982492,0.001482403,0.0000677933,0.0000235768,0.00006685763,0.000001401783,0.000001223029,0.000008079324,0.00009947734],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2630849,"threshold_uncertainty_score":0.7254706,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01231582787755334,"score_gpt":0.1997906943561128,"score_spread":0.1874748664785595,"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."}}