{"id":"W4391748599","doi":"10.1080/00036846.2024.2312260","title":"Volatility and dependence in crude oil and agricultural commodity markets","year":2024,"lang":"en","type":"article","venue":"Applied Economics","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Crude oil; Economics; Volatility (finance); Copula (linguistics); Bivariate analysis; Agriculture; Autoregressive conditional heteroskedasticity; Palm oil; Tail dependence; Commodity; Oil price; Econometrics; Sugar; Agricultural economics; Financial economics; Monetary economics; Environmental science; Agricultural science; Mathematics; Multivariate statistics; Chemistry; Statistics; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000868255,0.0001901359,0.000376475,0.0001153414,0.00007352832,0.0002235548,0.0001336829,0.0001434944,0.0001195885],"category_scores_gemma":[0.00002680586,0.0002098845,0.00004452087,0.0001052414,0.00009585009,0.0002486616,0.000156978,0.0002571224,0.00002335555],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001294158,"about_ca_system_score_gemma":0.00001530499,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002144114,"about_ca_topic_score_gemma":0.0008621922,"domain_scores_codex":[0.9985358,0.00001128968,0.0005319306,0.0006368779,0.00001433328,0.0002697662],"domain_scores_gemma":[0.9993919,0.0001556384,0.00009411342,0.000247874,0.000006315103,0.0001042306],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00006927879,0.00006963882,0.4641427,0.0003238169,0.00006343432,0.000003896846,0.0005455433,0.00001536195,0.00002178679,0.4799345,0.000226109,0.05458402],"study_design_scores_gemma":[0.0003424342,0.00001015083,0.6748888,0.00001557073,0.000004784667,0.000009158784,0.00006599753,0.2176792,0.000006818244,0.09541678,0.01122396,0.0003363652],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9436474,0.001600799,0.0001090866,0.0003679744,0.0001977857,0.00008608039,0.0001980344,0.00003999297,0.05375289],"genre_scores_gemma":[0.9975852,0.001450046,0.0004029107,0.00009157738,0.00004702339,0.00002836194,0.00002506639,0.00001596029,0.0003538957],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3845177,"threshold_uncertainty_score":0.8558841,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.014342297893383,"score_gpt":0.1968616755594518,"score_spread":0.1825193776660688,"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."}}