{"id":"W3044025017","doi":"10.1002/ijfe.1823","title":"Time‐dependent intrinsic correlation analysis of crude oil and the <scp>US</scp> dollar based on <scp>CEEMDAN</scp>","year":2020,"lang":"en","type":"article","venue":"International Journal of Finance & Economics","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"National Natural Science Foundation of China","keywords":"Econometrics; Economics; Correlation; Diversification (marketing strategy); Crude oil; Hilbert–Huang transform; Us dollar; Liberian dollar; Portfolio; Negative correlation; Mathematics; Statistics; Financial economics; White noise; Monetary economics; Exchange rate","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":[0.0004393692,0.0002517651,0.0002248361,0.001366742,0.0001983689,0.000412471,0.0001872317,0.0001953779,0.0009517001],"category_scores_gemma":[0.001839688,0.00009389003,0.0003488713,0.001180202,0.0001383675,0.0004934488,0.0003241042,0.0003371593,0.0001952615],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002576569,"about_ca_system_score_gemma":0.000330992,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008034062,"about_ca_topic_score_gemma":0.009030699,"domain_scores_codex":[0.9998388,0.00002386272,0.00001284621,0.0000411314,0.00005150381,0.00003176076],"domain_scores_gemma":[0.9991933,0.0001746137,0.00020905,0.0000774684,0.0002858708,0.00005978803],"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":[0.0006134241,0.0001615377,0.7763084,0.0001319439,0.000518718,0.00112805,0.0002251871,0.08779192,0.02950713,0.005030343,0.003672914,0.09491049],"study_design_scores_gemma":[0.000007052807,0.00004651871,0.6473444,0.00001939862,0.0001148531,0.0002112143,0.000136388,0.3420444,0.007593131,0.0008142435,0.001618862,0.0000496408],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9913082,0.00009223701,0.007066694,0.00005736293,0.00002212223,0.000005178013,0.0005254381,0.0000576828,0.0008651749],"genre_scores_gemma":[0.9973091,0.00007239934,0.001416652,0.000007786804,0.00001411973,0.000003814412,0.0007967537,0.00001130849,0.0003680929],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008034062,"threshold_uncertainty_score":0.01597458,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0115911327825275,"score_gpt":0.2047377439614359,"score_spread":0.1931466111789084,"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."}}