{"id":"W4386246290","doi":"10.18280/mmep.100423","title":"An Examination of Cryptocurrency Volatility: Insights from Skewed Error Innovation Distributions Within GARCH Model Frameworks","year":2023,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Cryptocurrency; Autoregressive conditional heteroskedasticity; Volatility (finance); Econometrics; Economics; 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":[0.004049266,0.0003373641,0.0004665115,0.001448088,0.0002769122,0.001758564,0.0004606544,0.0005796139,0.001021264],"category_scores_gemma":[0.01979499,0.0001788081,0.0005104304,0.0012528,0.0009200076,0.002100181,0.0009878832,0.00138277,0.0001085539],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006417542,"about_ca_system_score_gemma":0.0007317017,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003337421,"about_ca_topic_score_gemma":0.002419791,"domain_scores_codex":[0.9992169,0.0003216615,0.00004801972,0.00009818639,0.0002316063,0.00008365026],"domain_scores_gemma":[0.9902272,0.006926255,0.001440258,0.0007186081,0.0005278348,0.000159884],"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":[0.0002293479,0.0001111782,0.2435168,0.0001713578,0.0002527096,0.001316563,0.001860871,0.3536391,0.004718221,0.3224852,0.001658146,0.07004057],"study_design_scores_gemma":[0.00001376484,0.0000961734,0.0645855,0.00007700134,0.00004595785,0.0003714703,0.0008942015,0.804912,0.001192626,0.1254559,0.002305419,0.00004993314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8895472,0.0006221993,0.1009234,0.001017741,0.0000328066,0.00002821931,0.0002347125,0.0001163995,0.007477391],"genre_scores_gemma":[0.996651,0.0002352643,0.002767739,0.00002812247,0.00001812491,0.000005115859,0.00006894486,0.00001366925,0.0002119655],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004049266,"threshold_uncertainty_score":0.02141488,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05058510896514325,"score_gpt":0.2427981714944349,"score_spread":0.1922130625292916,"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."}}