{"id":"W2015949929","doi":"10.5555/1030453.1030674","title":"Derivatives and credit risk: credit risk modeling for catastrophic events","year":2002,"lang":"en","type":"article","venue":"Winter Simulation Conference","topic":"Credit Risk and Financial Regulations","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Credit risk; Asset (computer security); Computer science; Actuarial science; Probability of default; Jump; Jump diffusion; Credit valuation adjustment; Value (mathematics); Risk analysis (engineering); Econometrics; Economics; Business; Credit reference; Machine learning; Computer security","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":[0.001428944,0.0007681854,0.0007795536,0.0005368256,0.0006065455,0.001531136,0.001281498,0.001997955,0.00383864],"category_scores_gemma":[0.006430576,0.0003272403,0.0006301275,0.001056072,0.001179439,0.002625973,0.0009972958,0.00164629,0.0004502404],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007336278,"about_ca_system_score_gemma":0.0009184806,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008499578,"about_ca_topic_score_gemma":0.004965045,"domain_scores_codex":[0.999604,0.0001940416,0.0000196846,0.00005538408,0.00008753433,0.00003927885],"domain_scores_gemma":[0.9989128,0.000614415,0.0001566191,0.0000875025,0.0001364318,0.00009217083],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003831081,0.00003770355,0.002281733,0.00004660529,0.0000255005,0.0001701087,0.0001186605,0.8244644,0.0003702721,0.1577884,0.002947826,0.01171051],"study_design_scores_gemma":[0.000007109174,0.00001165209,0.0003379759,0.00001370052,0.000008245795,0.00005411055,0.00002163765,0.9047056,0.00008603992,0.09322145,0.001522182,0.00001021302],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08493076,0.003737141,0.8809446,0.006298618,0.0002942062,0.00007126782,0.0005330105,0.0003998538,0.02279053],"genre_scores_gemma":[0.9604898,0.002112971,0.02665271,0.0001778946,0.0002208984,0.00008488004,0.0001859372,0.00005384396,0.01002105],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008499578,"threshold_uncertainty_score":0.01690018,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07983630464915459,"score_gpt":0.257357384501222,"score_spread":0.1775210798520674,"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."}}