{"id":"W3122267907","doi":"","title":"Using Market Information for Banking System Risk Assessment","year":2005,"lang":"en","type":"article","venue":"RePEc: Research Papers in Economics","topic":"Banking stability, regulation, efficiency","field":"Economics, Econometrics and Finance","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Insolvency; Systemic risk; Ceteris paribus; Stress test; Asset (computer security); Interbank lending market; Actuarial science; Stress testing (software); Credit risk; Business; Default; Economics; Probability of default; Econometrics; Monetary economics; Financial crisis; Computer science; Market liquidity; Finance; Microeconomics","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":[0.004019438,0.0009066276,0.0009978076,0.006717502,0.000433665,0.002501691,0.0009652684,0.001206576,0.004623503],"category_scores_gemma":[0.02261063,0.0004040897,0.0009688797,0.00373228,0.0006528203,0.00429989,0.001370727,0.001465783,0.0009030388],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007912517,"about_ca_system_score_gemma":0.0007572742,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002589767,"about_ca_topic_score_gemma":0.002233686,"domain_scores_codex":[0.9976218,0.0007702177,0.0001658709,0.000385393,0.0009264163,0.0001301959],"domain_scores_gemma":[0.9868327,0.00833574,0.002155235,0.001194727,0.001214566,0.0002670451],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002662141,0.0004064936,0.07197122,0.0002624215,0.0005672914,0.0003826809,0.0004833653,0.2397751,0.004654132,0.1673723,0.006582886,0.5072759],"study_design_scores_gemma":[0.00002868031,0.0001132184,0.01417861,0.00004774587,0.00005365602,0.0001513065,0.00005835181,0.8509579,0.001857769,0.1277235,0.004728853,0.0001005659],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05103922,0.0004473947,0.9393067,0.0003847689,0.00006243355,0.000159428,0.001691232,0.000710964,0.00619783],"genre_scores_gemma":[0.7569711,0.0005059448,0.2374841,0.0001118705,0.0003063996,0.0003063784,0.001991017,0.00009660758,0.002226694],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006717502,"threshold_uncertainty_score":0.0212571,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03818855451507853,"score_gpt":0.3084631658627915,"score_spread":0.270274611347713,"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."}}