{"id":"W2560004879","doi":"","title":"Evaluation of value at risk: An empirical likelihood approach","year":2010,"lang":"en","type":"article","venue":"HKBU Institutional Repository (Hong Kong Baptist University)","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Value at risk; Empirical likelihood; Value (mathematics); Econometrics; Computer science; Maximum likelihood; Mathematics; Statistics; Actuarial science; Risk management; Economics; Confidence interval","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":[0.01195691,0.001292313,0.001604293,0.002751461,0.000444236,0.003116245,0.002439571,0.001983021,0.005211392],"category_scores_gemma":[0.06779905,0.0005734499,0.00111307,0.001496837,0.001845246,0.006484014,0.002496358,0.002624859,0.0004713763],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001286144,"about_ca_system_score_gemma":0.001548535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001326929,"about_ca_topic_score_gemma":0.0006815143,"domain_scores_codex":[0.9940323,0.004040888,0.00019934,0.0004364797,0.001088196,0.0002028446],"domain_scores_gemma":[0.9697098,0.02570957,0.00142653,0.001162872,0.001491833,0.0004995816],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009931929,0.0001158458,0.004222054,0.0002169543,0.0001953725,0.0003108794,0.0002315541,0.4576313,0.001220854,0.4589512,0.001908265,0.07489647],"study_design_scores_gemma":[0.00001072381,0.00003763617,0.0002985377,0.00003406182,0.00001454176,0.00006053788,0.00002677048,0.8775161,0.000324314,0.1210624,0.0005957424,0.00001855029],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007166872,0.0002869693,0.9902616,0.0003931139,0.00002059072,0.00003433127,0.00003694441,0.00009110534,0.001708382],"genre_scores_gemma":[0.6080948,0.001393974,0.3840842,0.000222436,0.0002566452,0.0003556084,0.0003695464,0.0002415098,0.004981371],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01195691,"threshold_uncertainty_score":0.06323498,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04134675257587277,"score_gpt":0.2365703237680874,"score_spread":0.1952235711922146,"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."}}