{"id":"W4313889981","doi":"10.3390/jrfm16010038","title":"An Empirical Examination of Asymmetry on Exchange Rate Spread Using the Quantile Autoregressive Distributed Lag (QARDL) Model","year":2023,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ege Üniversitesi; Yonsei University","keywords":"Econometrics; Quantile; Distributed lag; Economics; Autoregressive model; Exchange rate; Inefficiency; Volatility (finance); Asymmetry; Stock exchange; Lag; Financial economics; Monetary economics; Computer science; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002160579,0.0001144102,0.000317135,0.0003018208,0.0001366475,0.00004477865,0.0001775853,0.0000698919,0.00001185345],"category_scores_gemma":[0.000162237,0.00009214757,0.0001038024,0.0003260996,0.00005006345,0.0001489334,0.00007115908,0.0001771009,0.000002049963],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005946093,"about_ca_system_score_gemma":0.00001287763,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003537142,"about_ca_topic_score_gemma":0.0000149819,"domain_scores_codex":[0.9989336,0.00007153869,0.0005667245,0.0001799769,0.00008264394,0.0001655583],"domain_scores_gemma":[0.9988219,0.00009064851,0.0007644958,0.0002038472,0.00006502558,0.00005407042],"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":[0.001020445,0.001223236,0.5079841,0.0005589816,0.0002719259,0.0001382849,0.005746139,0.03790348,0.00004598776,0.1529278,0.003675443,0.2885042],"study_design_scores_gemma":[0.0003462512,0.0001129078,0.4607733,0.00002962329,0.00002216489,8.829545e-7,0.00010887,0.5252234,0.000004741747,0.01136011,0.001940414,0.00007738803],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8840294,0.000227584,0.114438,0.00008449495,0.0002574438,0.0001342365,0.0003019679,0.000007393947,0.0005194902],"genre_scores_gemma":[0.9979864,0.001254846,0.000552199,0.00004296966,0.00008598914,0.000002697099,0.00001251752,0.00000989711,0.00005252713],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4873199,"threshold_uncertainty_score":0.3757669,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03928679765913621,"score_gpt":0.2771189782882081,"score_spread":0.2378321806290719,"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."}}