{"id":"W4320479902","doi":"10.1016/j.jempfin.2023.01.002","title":"Forecasting tail risk measures for financial time series: An extreme value approach with covariates","year":2023,"lang":"en","type":"article","venue":"Journal of Empirical Finance","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"Russian Science Foundation; Australian Research Council; Monash University","keywords":"Autoregressive conditional heteroskedasticity; Econometrics; Covariate; Value at risk; Extreme value theory; Economics; Tail risk; Financial distress; Expected shortfall; Equity (law); Financial risk; Actuarial science; Risk management; Finance; Statistics; Mathematics; Volatility (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":[0.006244197,0.001238336,0.002232577,0.001599346,0.0004409558,0.001908563,0.001956864,0.002619284,0.001485132],"category_scores_gemma":[0.02129626,0.001020824,0.001635656,0.001724316,0.00084551,0.003672143,0.0013176,0.002643727,0.0002762129],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006698983,"about_ca_system_score_gemma":0.0006995514,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003884218,"about_ca_topic_score_gemma":0.003487421,"domain_scores_codex":[0.9988381,0.0005489938,0.00007777697,0.0002804293,0.0001365704,0.0001181083],"domain_scores_gemma":[0.9889025,0.008537067,0.0009892622,0.0007337186,0.0005558241,0.0002815109],"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":[0.0001527231,0.0001617539,0.01435891,0.00006742217,0.0001678303,0.0001611096,0.00007759481,0.9130942,0.0009059238,0.0342112,0.0009328265,0.03570848],"study_design_scores_gemma":[0.000005079469,0.00002068487,0.0007932237,0.000005998786,0.00001140428,0.000008568838,0.000005838694,0.9881085,0.00008630755,0.0108698,0.0000771219,0.000007603387],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09889272,0.0004339656,0.8992561,0.0003535861,0.00005761213,0.00003850993,0.0002786547,0.0002615341,0.0004273609],"genre_scores_gemma":[0.9236309,0.0006874764,0.07239151,0.00009063562,0.0002155177,0.0001002121,0.0008996139,0.00006172754,0.001922401],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006244197,"threshold_uncertainty_score":0.03302288,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1382217294197501,"score_gpt":0.2799303976517136,"score_spread":0.1417086682319634,"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."}}