{"id":"W2510662069","doi":"10.5539/ijef.v8n9p117","title":"Empirical Analysis of “Volatilitysurprise” between Dollar Exchange Rate and CRB Commodity Future Markets","year":2016,"lang":"en","type":"article","venue":"International Journal of Economics and Finance","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Economics; Equity (law); Volatility (finance); Econometrics; Exchange rate; Us dollar; Liberian dollar; Surprise; U.S. Dollar Index; Index (typography); Financial economics; Commodity; Monetary economics; Finance","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.002458498,0.0004051646,0.0006001061,0.001217169,0.000324511,0.001552555,0.0005629717,0.0007343171,0.001726549],"category_scores_gemma":[0.01371323,0.0003148612,0.001004916,0.001499584,0.0005086208,0.002100714,0.0008769663,0.001368709,0.0001806424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006941301,"about_ca_system_score_gemma":0.0006188066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005280392,"about_ca_topic_score_gemma":0.002984965,"domain_scores_codex":[0.9989917,0.0002280377,0.0000810469,0.0002348195,0.0002954465,0.0001688956],"domain_scores_gemma":[0.9924411,0.004585312,0.001695581,0.0004310015,0.0005103701,0.0003366878],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002312719,0.0001908474,0.9178032,0.00008087002,0.0005291685,0.001070998,0.0005132875,0.03951029,0.001989277,0.01441688,0.001492398,0.02217167],"study_design_scores_gemma":[0.0000296152,0.000203708,0.6443012,0.00003364991,0.0003033317,0.0007372833,0.0007168783,0.3399506,0.00167714,0.009924687,0.002036314,0.00008566574],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9937345,0.0003507694,0.003929466,0.0003367308,0.00001825225,0.000009776885,0.0001863998,0.00004412614,0.001389866],"genre_scores_gemma":[0.9989579,0.0001182882,0.0003217757,0.00001640585,0.00002118927,0.000003972084,0.0002706168,0.000005465467,0.0002843479],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005280392,"threshold_uncertainty_score":0.01300192,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03032967778641061,"score_gpt":0.2580227549338192,"score_spread":0.2276930771474086,"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."}}