{"id":"W2047793472","doi":"10.1016/j.jempfin.2011.05.006","title":"Modeling and forecasting expected shortfall with the generalized asymmetric Student-t and asymmetric exponential power distributions","year":2011,"lang":"en","type":"article","venue":"Journal of Empirical Finance","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":88,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Econometrics; Expected shortfall; Autoregressive conditional heteroskedasticity; Skewness; Volatility (finance); Downside risk; Conditional variance; Kurtosis; Value at risk; Heavy-tailed distribution; Tail risk; Economics; Variance (accounting); Mathematics; Statistics; Probability distribution; Risk management; Financial economics; 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":[0.003115682,0.0007883478,0.001252674,0.0008801319,0.0003249717,0.001222039,0.001445226,0.001567521,0.0009645316],"category_scores_gemma":[0.01351429,0.0007773275,0.001160882,0.0008215694,0.0007330655,0.003106368,0.0007525427,0.001445365,0.0001752942],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006494471,"about_ca_system_score_gemma":0.00062234,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004693551,"about_ca_topic_score_gemma":0.0029645,"domain_scores_codex":[0.9994447,0.0002406931,0.00003920986,0.0001141049,0.00008792311,0.00007345676],"domain_scores_gemma":[0.9942948,0.004303237,0.0005766089,0.0001986931,0.0004614189,0.0001651351],"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":[0.00005550545,0.00002728092,0.00185122,0.0000154492,0.00002985757,0.00006568544,0.00002879975,0.979533,0.0003159227,0.01123639,0.0002084962,0.006632502],"study_design_scores_gemma":[0.000002249487,0.000003526676,0.00007537677,6.843727e-7,0.000001838615,0.000004719034,0.000001549587,0.9975293,0.00003479994,0.002326502,0.00001761639,0.000001860046],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2963449,0.0003743226,0.7015797,0.0003492235,0.00006891741,0.00002909191,0.00008463319,0.0001512195,0.00101794],"genre_scores_gemma":[0.9693897,0.000270645,0.02840334,0.00003832853,0.00007745869,0.00003155333,0.0001223431,0.00003926988,0.001627368],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004693551,"threshold_uncertainty_score":0.01647753,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09692563840057256,"score_gpt":0.2770696717324108,"score_spread":0.1801440333318382,"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."}}