{"id":"W3011783965","doi":"10.3390/risks8010025","title":"Importance Sampling in the Presence of PD-LGD Correlation","year":2020,"lang":"en","type":"article","venue":"Risks","topic":"Credit Risk and Financial Regulations","field":"Economics, Econometrics and Finance","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; Wilfrid Laurier University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Default; Loss given default; Portfolio; Correlation; Econometrics; Sampling (signal processing); Importance sampling; Statistics; Mathematics; Computer science; Economics; Monte Carlo method; Financial economics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.009320698,0.001023656,0.002137836,0.001242504,0.0006589345,0.00165456,0.002392473,0.001521443,0.002039278],"category_scores_gemma":[0.0440588,0.001116258,0.001162139,0.001181037,0.002311323,0.002550966,0.00240088,0.002932915,0.0003037439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001565326,"about_ca_system_score_gemma":0.001985904,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007082566,"about_ca_topic_score_gemma":0.004580871,"domain_scores_codex":[0.9964148,0.002001261,0.0001519893,0.0005773378,0.0005866645,0.0002678829],"domain_scores_gemma":[0.951781,0.04265494,0.001918148,0.00154289,0.001497444,0.0006054525],"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":[0.0002755426,0.0001034037,0.00556878,0.0002023653,0.0001145726,0.0003270682,0.0001824052,0.8386253,0.0008271473,0.1189818,0.00149936,0.03329224],"study_design_scores_gemma":[0.00001621623,0.0000206054,0.00028497,0.00001550453,0.000007856958,0.00003002955,0.00001102742,0.9700706,0.0001985172,0.0290859,0.0002509423,0.000007872771],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02130033,0.0003418933,0.9768837,0.000281553,0.00003998495,0.00007854998,0.00005882718,0.0001368914,0.0008782348],"genre_scores_gemma":[0.7231788,0.0009304621,0.2705255,0.0004343991,0.0002628198,0.0004271167,0.0005272604,0.0001226048,0.003591035],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009320698,"threshold_uncertainty_score":0.0492931,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1456456161142634,"score_gpt":0.2894284327285236,"score_spread":0.1437828166142602,"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."}}