Instabilities in Cox proportional hazards models in credit risk

IF 0.3 4区 经济学 Q4 Economics, Econometrics and Finance Journal of Credit Risk Pub Date : 2023-01-01 DOI:10.21314/jcr.2022.014
J. Breeden, A. Bellotti, Ye. A. Leonova
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信用风险中Cox比例风险模型的不稳定性
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来源期刊
Journal of Credit Risk
Journal of Credit Risk BUSINESS, FINANCE-
CiteScore
0.90
自引率
0.00%
发文量
10
期刊介绍: With the re-writing of the Basel accords in international banking and their ensuing application, interest in credit risk has never been greater. The Journal of Credit Risk focuses on the measurement and management of credit risk, the valuation and hedging of credit products, and aims to promote a greater understanding in the area of credit risk theory and practice. The Journal of Credit Risk considers submissions in the form of research papers and technical papers, on topics including, but not limited to: Modelling and management of portfolio credit risk Recent advances in parameterizing credit risk models: default probability estimation, copulas and credit risk correlation, recoveries and loss given default, collateral valuation, loss distributions and extreme events Pricing and hedging of credit derivatives Structured credit products and securitizations e.g. collateralized debt obligations, synthetic securitizations, credit baskets, etc. Measuring managing and hedging counterparty credit risk Credit risk transfer techniques Liquidity risk and extreme credit events Regulatory issues, such as Basel II, internal ratings systems, credit-scoring techniques and credit risk capital adequacy.
期刊最新文献
Understanding and predicting systemic corporate distress: a machine-learning approach Managerial connections and corporate risk-taking: evidence from the Great Recession Instabilities in Cox proportional hazards models in credit risk Calibration alternatives to logistic regression and their potential for transferring the statistical dispersion of discriminatory power into uncertainties in probabilities of default Small and medium-sized enterprises’ time to default: an analysis using an improved mixture cure model with time-varying covariates
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