{"id":"W4230943617","doi":"10.2139/ssrn.1343091","title":"Using Structural Models for Default Prediction","year":2009,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Credit Risk and Financial Regulations","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Econometrics; Credit risk; Leverage (statistics); Capital structure; Volatility (finance); Credit default swap; Default risk; Equity (law); Loss given default; Economics; Debt; Actuarial science; Capital requirement; Mathematics; Statistics; Finance; Profit (economics)","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":[0.004146775,0.0009318059,0.001621442,0.001609278,0.0006350012,0.002046085,0.00154442,0.002332311,0.007591648],"category_scores_gemma":[0.02721123,0.0009507205,0.001582523,0.001488538,0.0007282721,0.00358033,0.001220432,0.002614576,0.001431046],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001171371,"about_ca_system_score_gemma":0.0009688871,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008071831,"about_ca_topic_score_gemma":0.01241097,"domain_scores_codex":[0.9988441,0.0006528978,0.00006310573,0.000238515,0.00008876204,0.0001126532],"domain_scores_gemma":[0.9726678,0.02389842,0.001346948,0.001086095,0.0006601511,0.0003406285],"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":[0.0003203551,0.0003564681,0.02254236,0.0001011885,0.0003888497,0.0001974181,0.0002003172,0.8741384,0.0003350627,0.05704277,0.004099947,0.0402768],"study_design_scores_gemma":[0.00002574068,0.00001839551,0.0007309135,0.000006361418,0.00002520211,0.000008780557,0.00001098132,0.9584041,0.00004689826,0.04059349,0.0001234503,0.000005803859],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4205575,0.0009099091,0.5653482,0.003776579,0.0002822945,0.0001193177,0.001700665,0.001410863,0.005894612],"genre_scores_gemma":[0.9782784,0.00036425,0.0165207,0.000110426,0.0001806206,0.00008821149,0.001161114,0.00007721868,0.003219043],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008071831,"threshold_uncertainty_score":0.02539653,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0397746961303466,"score_gpt":0.256991345148577,"score_spread":0.2172166490182304,"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."}}