{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000235102,0.0001933331,0.0001977961,0.00170517,0.0007259667,0.001529979,0.0004775408,0.0001777133,0.0009810083],"category_scores_gemma":[0.00155426,0.00008453014,0.0003391065,0.00413592,0.0002189021,0.0003936971,0.0004081416,0.0003225435,0.0001033614],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006432712,"about_ca_system_score_gemma":0.006477324,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9164094,"about_ca_topic_score_gemma":0.9048704,"domain_scores_codex":[0.9997218,0.0000115126,0.00001002847,0.00003244686,0.0001465015,0.00007768225],"domain_scores_gemma":[0.9994338,0.00008105354,0.0001491727,0.000021706,0.0002404666,0.00007381699],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001970581,0.00008731009,0.9342234,0.0001808526,0.0002463557,0.0007301049,0.0012081,0.008501126,0.002191297,0.008279502,0.005303024,0.03885191],"study_design_scores_gemma":[0.000008871563,0.00002231089,0.9778552,0.00002345729,0.00006362022,0.0002396704,0.00110004,0.01439734,0.000947901,0.0006307588,0.004677448,0.00003328584],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9915596,0.0008076184,0.0002093685,0.0001832888,0.000007238808,0.00001138049,0.002878275,0.00002171302,0.004321511],"genre_scores_gemma":[0.9962729,0.000466594,0.0001825421,0.00001867996,0.000005340559,0.000003521767,0.002325035,0.000003151982,0.0007222525],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08359063,"threshold_uncertainty_score":0.1681657,"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."}}