{"id":"W3125683782","doi":"","title":"Modeling the Dynamics of Credit Spreads with Stochastic Volatility","year":2003,"lang":"en","type":"preprint","venue":"Tilburg University Research Portal","topic":"Credit Risk and Financial Regulations","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; McGill University","funders":"","keywords":"Stochastic volatility; Economics; Volatility (finance); iTraxx; Corporate bond; Credit derivative; Affine transformation; Econometrics; Bond; Credit spread (options); Credit risk; Welfare economics; Financial economics; Mathematics; Credit valuation adjustment; Actuarial science; Finance","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001461035,0.0002066222,0.0004991245,0.0004641244,0.0003857917,0.00005254032,0.0007297764,0.0002987658,0.0003612749],"category_scores_gemma":[0.0002157972,0.0002041079,0.0001907038,0.0005544127,0.0005500734,0.000141295,0.0005561146,0.001259182,0.00002602129],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002782386,"about_ca_system_score_gemma":0.0004315196,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003693235,"about_ca_topic_score_gemma":0.001473083,"domain_scores_codex":[0.9982529,0.00006882977,0.0004264332,0.0005829317,0.000212106,0.0004568095],"domain_scores_gemma":[0.9981585,0.0001269139,0.0002778122,0.0009041224,0.0003994233,0.0001332558],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002999521,0.0003329773,0.04735948,0.0001773885,0.0002892656,0.00005853645,0.00110049,0.1754745,0.000001479181,0.7722595,0.00209919,0.0005472166],"study_design_scores_gemma":[0.0004135385,0.0001059925,0.01389503,0.00008031404,0.00003166892,0.000004856055,0.001193624,0.9594713,0.000001112097,0.02323819,0.001248208,0.0003161624],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8519706,0.0003682345,0.113775,0.0003175179,0.0002925618,0.0006986587,0.001493448,0.00003297633,0.03105105],"genre_scores_gemma":[0.9955079,0.0001119382,0.00027053,0.000001587518,0.00009939542,0.000003441564,0.000178473,0.00002357114,0.003803174],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7839968,"threshold_uncertainty_score":0.8323277,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07552246507677221,"score_gpt":0.2743967098788387,"score_spread":0.1988742448020665,"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."}}