{"id":"W3156888768","doi":"10.1002/cjs.11614","title":"Quasi‐maximum exponential likelihood estimation for double‐threshold GARCH models","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China; Natural Science Foundation of Jilin Province","keywords":"Autoregressive conditional heteroskedasticity; Heteroscedasticity; Estimator; Mathematics; Autoregressive model; Econometrics; Applied mathematics; Maximum likelihood; Exponential function; Series (stratigraphy); Inference; Nonparametric statistics; Statistics; Computer science; Volatility (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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005927216,0.0003480432,0.0008731725,0.0006944507,0.0002413197,0.001220537,0.001375113,0.0009148747,0.001972148],"category_scores_gemma":[0.02337,0.0005003129,0.0008294374,0.0008290467,0.0008740266,0.002048734,0.001344119,0.001205397,0.0002080758],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006079342,"about_ca_system_score_gemma":0.0007762849,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002178929,"about_ca_topic_score_gemma":0.001772698,"domain_scores_codex":[0.9985589,0.0008887259,0.00006471771,0.0001623328,0.0002392108,0.00008599072],"domain_scores_gemma":[0.9888975,0.009354771,0.0005810537,0.000618286,0.0004425536,0.0001058799],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001538411,0.00006531024,0.004061165,0.0001789268,0.0001393358,0.0002008576,0.0001432005,0.7910323,0.002368812,0.1542511,0.0008703541,0.04653479],"study_design_scores_gemma":[0.000007634007,0.000009035639,0.000355572,0.000007495696,0.000003856886,0.0000172276,0.000007093223,0.9752548,0.0002197774,0.02395174,0.0001601285,0.000005684593],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02690853,0.0001548068,0.9721242,0.0001525796,0.00001689907,0.00001393426,0.00005011066,0.00007365636,0.0005053669],"genre_scores_gemma":[0.8168763,0.0002901874,0.1809109,0.00007765245,0.00005828082,0.00009823719,0.000255431,0.00005542885,0.001377756],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005927216,"threshold_uncertainty_score":0.0313465,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06704527541072851,"score_gpt":0.247214473933424,"score_spread":0.1801691985226955,"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."}}