{"id":"W4313855166","doi":"10.1515/snde-2022-0029","title":"Volatility and dependence in cryptocurrency and financial markets: a copula approach","year":2023,"lang":"en","type":"article","venue":"Studies in Nonlinear Dynamics and Econometrics","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Cryptocurrency; Economics; Copula (linguistics); Econometrics; Volatility (finance); Autoregressive conditional heteroskedasticity; Stock (firearms); Tail dependence; Financial market; Financial economics; Statistics; Mathematics; Finance","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":[0.002218476,0.0007176002,0.001194787,0.001444866,0.0004524463,0.001926607,0.0009541715,0.001099467,0.003142618],"category_scores_gemma":[0.01102465,0.000663359,0.001525371,0.001030929,0.001029539,0.002027324,0.0009630329,0.001680244,0.0003527682],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000835478,"about_ca_system_score_gemma":0.0008835942,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01150711,"about_ca_topic_score_gemma":0.004986799,"domain_scores_codex":[0.9993332,0.0003002619,0.00002592338,0.0001243164,0.0001079941,0.000108211],"domain_scores_gemma":[0.9938398,0.004607283,0.0006679151,0.0002912217,0.000347648,0.000246103],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001379475,0.0001974121,0.03435634,0.0001097203,0.0007294768,0.000871562,0.0002819913,0.7865615,0.003201848,0.1562538,0.002230376,0.01506807],"study_design_scores_gemma":[0.00000580515,0.00001780129,0.003714036,0.000007862971,0.00003280002,0.00004537298,0.00002388276,0.9816047,0.0001567568,0.01416332,0.0002133619,0.00001438335],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6835865,0.0011902,0.3054752,0.001497483,0.00007020371,0.00006939375,0.00046477,0.0002993545,0.007346877],"genre_scores_gemma":[0.9910773,0.0003877372,0.005615188,0.00007746125,0.0000505391,0.00002518696,0.0001605253,0.00005630134,0.002549812],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01150711,"threshold_uncertainty_score":0.02288026,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0606410353323016,"score_gpt":0.2780736397478486,"score_spread":0.217432604415547,"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."}}