{"id":"W4323845856","doi":"10.3390/jrfm16030187","title":"On Asymmetric Correlations and Their Applications in Financial Markets","year":2023,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Copula (linguistics); Econometrics; Economics; Diversification (marketing strategy); Asymmetry; Test statistic; Value at risk; Portfolio; Information asymmetry; Autoregressive conditional heteroskedasticity; Financial market; Statistic; Bivariate analysis; Financial economics; Mathematics; Statistical hypothesis testing; Statistics; Volatility (finance); Risk management; Physics; Finance; Business","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":[0.00109415,0.0001452781,0.000370121,0.001206585,0.0001908176,0.00004397205,0.0001262138,0.00009520059,0.000006877267],"category_scores_gemma":[0.0004138938,0.0001402248,0.00009062683,0.001083933,0.00004227507,0.0001544958,0.00007875742,0.0002990219,0.00002663728],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005178475,"about_ca_system_score_gemma":0.00001785647,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004493528,"about_ca_topic_score_gemma":0.00003514132,"domain_scores_codex":[0.9987572,0.00002120943,0.0006992495,0.000242494,0.00005392813,0.0002259588],"domain_scores_gemma":[0.9991654,0.0002052093,0.0003793383,0.0001450609,0.00003305809,0.00007199707],"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":[0.0001204415,0.0001664288,0.1631634,0.00005776259,0.00001358018,0.00001754917,0.001107101,0.0006228035,5.031033e-7,0.5566326,0.001193202,0.2769047],"study_design_scores_gemma":[0.0006595255,0.00008273342,0.7127425,0.0000405892,0.000008522884,0.000001920103,0.0001203654,0.003249303,0.000001122473,0.2440779,0.03888,0.0001354965],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8780468,0.002816458,0.1149649,0.0001681726,0.0004185421,0.0003550707,0.0001061826,0.00002045352,0.003103424],"genre_scores_gemma":[0.9919186,0.007224356,0.000515416,0.00006697373,0.0001199393,0.00001786543,0.000004593772,0.00001207483,0.0001201661],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5495791,"threshold_uncertainty_score":0.57182,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0153661739197592,"score_gpt":0.2122679535318112,"score_spread":0.196901779612052,"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."}}