{"id":"W2537308954","doi":"10.1111/eufm.12096","title":"The Role of the Conditional Skewness and Kurtosis in VIX Index Valuation","year":2016,"lang":"en","type":"article","venue":"European Financial Management","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"HEC Montréal","funders":"","keywords":"Kurtosis; Skewness; Econometrics; Normal-inverse Gaussian distribution; Gaussian; Stock market index; Index (typography); Economics; Mathematics; Statistics; Stock market; Computer science; Gaussian random field; Gaussian function; Geography; 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":[0.00410705,0.0004371329,0.0004858266,0.00133117,0.0003134553,0.004289632,0.000843485,0.0009265795,0.001839999],"category_scores_gemma":[0.03391301,0.0002671675,0.0004372992,0.0008376848,0.001546967,0.003742626,0.001319414,0.001264638,0.000218872],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009903484,"about_ca_system_score_gemma":0.0006982606,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002577308,"about_ca_topic_score_gemma":0.001179548,"domain_scores_codex":[0.9991373,0.0003428766,0.00006662114,0.0001047894,0.0002266451,0.000121796],"domain_scores_gemma":[0.9842606,0.009840606,0.003061722,0.0009885231,0.001210753,0.0006378287],"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":[0.0004449207,0.0001385276,0.1138677,0.0001036993,0.0001960889,0.001388419,0.0004866651,0.4237371,0.009504689,0.4124255,0.001971876,0.03573475],"study_design_scores_gemma":[0.00001193431,0.00006249221,0.02882222,0.00005073429,0.00002560296,0.0002439886,0.0001282603,0.8404469,0.001204765,0.1284124,0.0005214202,0.00006927899],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8485501,0.0009766476,0.1330024,0.001749399,0.0001296854,0.00003555882,0.0002494061,0.000170438,0.01513632],"genre_scores_gemma":[0.9982247,0.0001212991,0.001146297,0.00001637841,0.00003219738,0.000003342401,0.00003180619,0.00001366413,0.0004103532],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004289632,"threshold_uncertainty_score":0.02172041,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01483749674220499,"score_gpt":0.1852475073722125,"score_spread":0.1704100106300075,"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."}}