{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002640942,0.0003399326,0.0009346505,0.0009732953,0.000281089,0.0001535604,0.000448984,0.0001687412,0.00001719258],"category_scores_gemma":[0.001382585,0.0003444535,0.0003054425,0.001022135,0.00008266764,0.0004087682,0.0002362156,0.0005361214,0.0001307054],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003046129,"about_ca_system_score_gemma":0.00007114633,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004403713,"about_ca_topic_score_gemma":0.00008569007,"domain_scores_codex":[0.9972121,0.00008113585,0.001449532,0.0004998661,0.0001972336,0.0005601159],"domain_scores_gemma":[0.9976929,0.00005047923,0.001214253,0.0003651573,0.0002908744,0.0003862932],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002063304,0.001470059,0.1627398,0.0001670656,0.0003150236,0.0001825212,0.006749217,0.002425227,0.000002124837,0.6233212,0.04150223,0.1590622],"study_design_scores_gemma":[0.003959032,0.0008152951,0.2794075,0.00009613782,0.0001678151,0.00002031381,0.0007274247,0.000396746,0.00001748228,0.07229123,0.64143,0.0006710635],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5832602,0.005896301,0.3897815,0.0003124258,0.002438634,0.0009222038,0.000547881,0.00005204291,0.01678875],"genre_scores_gemma":[0.9886788,0.004417712,0.005430025,0.0004532661,0.000712364,0.00003906231,0.00001033199,0.00003059855,0.0002278665],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5999277,"threshold_uncertainty_score":0.9999008,"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."}}