{"id":"W1043144703","doi":"10.1016/j.insmatheco.2015.06.009","title":"Risk concentration based on Expectiles for extreme risks under FGM copula","year":2015,"lang":"en","type":"article","venue":"Insurance Mathematics and Economics","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":28,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Copula (linguistics); Diversification (marketing strategy); Operational risk; Econometrics; Context (archaeology); Computer science; Mathematics; Risk management; Economics; Business; Geography","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":[0.005711751,0.001367448,0.001842169,0.002405994,0.0006671962,0.002528999,0.001918383,0.001783666,0.002731293],"category_scores_gemma":[0.02750436,0.0006644704,0.001654167,0.001125867,0.00179883,0.004344077,0.002389587,0.002534996,0.0003459443],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001176313,"about_ca_system_score_gemma":0.0007170769,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001825684,"about_ca_topic_score_gemma":0.0009183979,"domain_scores_codex":[0.9984162,0.0007432671,0.00006469995,0.0003008467,0.0002382348,0.0002366729],"domain_scores_gemma":[0.9882218,0.007583389,0.001429839,0.0007445139,0.001173281,0.0008472177],"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":[0.0001651527,0.00008682419,0.008084542,0.000181278,0.0001754404,0.0005033403,0.00035375,0.4546048,0.002429349,0.5113018,0.002555975,0.01955782],"study_design_scores_gemma":[0.00000485619,0.00003098922,0.001423569,0.0000272335,0.00002397547,0.0001227013,0.00003023073,0.9311252,0.0003090927,0.06656249,0.0003170373,0.00002270798],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.112917,0.001015835,0.8793282,0.0007046014,0.00007725603,0.00007113535,0.0001750174,0.0002459977,0.005464899],"genre_scores_gemma":[0.9547375,0.0009324176,0.03844225,0.0002274558,0.0003333537,0.0001493592,0.0003538835,0.0001437943,0.004680027],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005711751,"threshold_uncertainty_score":0.03020698,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1774610582710164,"score_gpt":0.272480224957822,"score_spread":0.09501916668680563,"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."}}