{"id":"W2111859414","doi":"10.1002/fut.21684","title":"Valuing Retail Credit Tranches with Structural, Double Mixture Models","year":2014,"lang":"en","type":"article","venue":"Journal of Futures Markets","topic":"Credit Risk and Financial Regulations","field":"Economics, Econometrics and Finance","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"IBM (Canada); University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Tranche; Conditional independence; Econometrics; Portfolio; Economics; Class (philosophy); Measure (data warehouse); Homogeneous; Independence (probability theory); Credit risk; Mixing (physics); Mathematical economics; Mathematics; Statistical physics; Actuarial science; Financial economics; Computer science; Statistics; Physics; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006972168,0.0001693815,0.0004710033,0.0002305373,0.0001808793,0.0001040048,0.0002734916,0.0001333836,0.0002058911],"category_scores_gemma":[0.00007207985,0.0001379605,0.0001954352,0.0001935716,0.0000551218,0.0005287739,0.00001927623,0.0003250761,0.000007335416],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005597074,"about_ca_system_score_gemma":0.00003602939,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001839603,"about_ca_topic_score_gemma":0.00002742521,"domain_scores_codex":[0.9987713,0.00001971503,0.0006604499,0.0001930619,0.0001140889,0.0002414353],"domain_scores_gemma":[0.9987001,0.00006735788,0.0007526692,0.0002318151,0.0001253061,0.000122703],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.005127721,0.0002837045,0.1247687,0.0002790565,0.0008409304,0.00009411778,0.006346665,0.03013277,0.0003499677,0.6640331,0.07154933,0.09619398],"study_design_scores_gemma":[0.0042625,0.0005110164,0.6094418,0.0001533604,0.00007616257,0.0002526159,0.0001902888,0.01587107,0.0001738887,0.1311632,0.2372574,0.0006467351],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9574442,0.003522343,0.01784712,0.001079024,0.001358443,0.0001125162,0.00002718787,0.00001730724,0.01859187],"genre_scores_gemma":[0.9939173,0.000235199,0.003343665,0.00004639659,0.001883126,0.00000206782,0.000003850859,0.00002435679,0.0005440767],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5328699,"threshold_uncertainty_score":0.5625867,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02312093257013943,"score_gpt":0.2086497085452977,"score_spread":0.1855287759751583,"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."}}