{"id":"W1974988607","doi":"10.3390/jrfm8020198","title":"Interconnected Risk Contributions: A Heavy-Tail Approach to Analyze U.S. Financial Sectors","year":2015,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Insurance and Financial Risk Management","field":"Economics, Econometrics and Finance","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Sapienza Università di Roma","keywords":"Stylized fact; Expected shortfall; Tail risk; Actuarial science; Financial stability; Multivariate statistics; Financial services; Financial sector; Economics; Financial risk; Value (mathematics); Econometrics; Business; Risk management; Finance; Financial system; Statistics","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":[0.00110087,0.0003278792,0.0003893276,0.002623947,0.000517429,0.001202023,0.0004551425,0.000607077,0.002266205],"category_scores_gemma":[0.005014581,0.0001969887,0.0007460645,0.002162618,0.0005609526,0.001458899,0.001466856,0.0009078385,0.000245013],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006140159,"about_ca_system_score_gemma":0.0003865182,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006600594,"about_ca_topic_score_gemma":0.004956129,"domain_scores_codex":[0.9997467,0.00008883081,0.00001372944,0.00005105037,0.00004823507,0.00005140976],"domain_scores_gemma":[0.9981174,0.000724909,0.0005409734,0.0002058558,0.000173135,0.0002376261],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002328342,0.0002157068,0.492579,0.00008636517,0.0004859971,0.001113287,0.001396944,0.2379164,0.002902344,0.1778286,0.004259178,0.08098337],"study_design_scores_gemma":[0.00001342156,0.00009711469,0.151819,0.00004344662,0.00008702,0.0002649071,0.000739211,0.6692935,0.0007381845,0.1731192,0.003730321,0.00005467157],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8767519,0.001039151,0.113162,0.0007329543,0.00002291733,0.00006544298,0.0007846259,0.0001195806,0.007321484],"genre_scores_gemma":[0.9927531,0.0002701894,0.005618582,0.000048532,0.00002535902,0.00002021217,0.0003204673,0.00001374163,0.0009298463],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006600594,"threshold_uncertainty_score":0.01312435,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0152012052646239,"score_gpt":0.2134600406126112,"score_spread":0.1982588353479873,"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."}}