{"id":"W3125517721","doi":"","title":"Estimating the Structural Credit Risk Model When Equity Prices Are Contaminated by Trading Noises","year":2006,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Credit Risk and Financial Regulations","field":"Economics, Econometrics and Finance","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Equity (law); Econometrics; Market liquidity; Estimation; Credit risk; Volatility (finance); Economics; Likelihood function; Maximum likelihood; Financial economics; Monetary economics; Actuarial science; Statistics; Mathematics","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":[0.003429262,0.0005424341,0.0008705324,0.0005642494,0.0001988148,0.001009032,0.0008094246,0.001163051,0.0006485256],"category_scores_gemma":[0.0233942,0.0004613122,0.0005666186,0.0006943988,0.0007587915,0.001519717,0.000901169,0.001171652,0.0001567263],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006224363,"about_ca_system_score_gemma":0.0009871385,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007994406,"about_ca_topic_score_gemma":0.004513834,"domain_scores_codex":[0.9991099,0.0004248953,0.00004765447,0.0001825341,0.0001605983,0.00007448583],"domain_scores_gemma":[0.9902582,0.008184067,0.0006961381,0.0005049427,0.0002845628,0.00007207696],"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":[0.0001249352,0.00005195519,0.01007078,0.00004382942,0.00005835366,0.0001099109,0.00009018137,0.9440335,0.001116079,0.02282571,0.0002755019,0.02119925],"study_design_scores_gemma":[0.00001646444,0.00001339532,0.001095185,0.000002737423,0.000005429539,0.0000112935,0.000007130647,0.9895579,0.0004168757,0.008810976,0.00005669658,0.000005815199],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.31124,0.00009231063,0.6872072,0.0003049414,0.00001288557,0.00004370296,0.0001548858,0.0001471775,0.0007969335],"genre_scores_gemma":[0.9401434,0.0001095023,0.05861815,0.00004049305,0.00001626585,0.0000490425,0.0002678837,0.00001495103,0.0007404149],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007994406,"threshold_uncertainty_score":0.01813591,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05571146794473958,"score_gpt":0.3101579916306734,"score_spread":0.2544465236859338,"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."}}