{"id":"W4281629669","doi":"10.3390/jrfm15060240","title":"Dynamic Causality Analysis of COVID-19 Pandemic Risk and Oil Market Changes","year":2022,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Pandemic; Granger causality; Futures contract; Proxy (statistics); Coronavirus disease 2019 (COVID-19); Economics; Preparedness; Lag; Causality (physics); Distributed lag; Econometrics; Financial economics; Business; Medicine; Statistics; Internal medicine; Disease; Computer science","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":[0.001779747,0.0003403575,0.0003752876,0.001679969,0.0003209234,0.001255192,0.0003934874,0.0006176623,0.005309931],"category_scores_gemma":[0.01065836,0.0001700748,0.0007726015,0.00124003,0.0004679972,0.001379014,0.0007277781,0.001262609,0.0002568666],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005534123,"about_ca_system_score_gemma":0.000824329,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007094591,"about_ca_topic_score_gemma":0.004428706,"domain_scores_codex":[0.9994538,0.0001531285,0.00004670374,0.0001316216,0.0001051261,0.0001095788],"domain_scores_gemma":[0.9928272,0.004106976,0.001961863,0.0002478908,0.0005556796,0.0003003553],"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":[0.0002757302,0.000187934,0.889803,0.00008981168,0.0004717445,0.0009570466,0.0003344392,0.04529468,0.001458609,0.03148377,0.001317676,0.02832555],"study_design_scores_gemma":[0.00004532871,0.0003408204,0.4674024,0.00004963372,0.0002902349,0.0004474466,0.001398816,0.4815871,0.001927036,0.04342986,0.002997894,0.00008334936],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9825282,0.0004285052,0.01266502,0.0007904074,0.00003647066,0.00003837989,0.0004880885,0.00004540688,0.002979702],"genre_scores_gemma":[0.9983639,0.000139422,0.0005835799,0.00002164721,0.00001621214,0.000009498779,0.0002183086,0.00000360279,0.0006438176],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007094591,"threshold_uncertainty_score":0.0177635,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0179010588635317,"score_gpt":0.2388012153391154,"score_spread":0.2209001564755837,"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."}}