Journal of Credit Risk
ISSN:
1744-6619 (print)
1755-9723 (online)
Editor-in-chief: Linda Allen and Jens Hilscher
About this journal
With the adoption of machine learning and artificial intelligence in financial institutions, credit analysis methodologies and applications are rapidly evolving.
The Journal of Credit Risk is at the forefront in tackling the many issues and challenges posed by these novel technologies both in and out of periods of financial crisis. Topics include fintech, liquidity risk and the connection to credit risk, the valuation and hedging of credit products, and the promotion of 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 reports on, but not limited to, the following topics.
- Modeling 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.
- The pricing and hedging of credit derivatives.
- Structured credit products and securitizations, eg, collateralized debt obligations, synthetic securitizations, credit baskets, etc.
- Machine learning and artificial intelligence.
- Credit risk implications of blockchain, crypto currencies and fintech firms.
- Measuring, managing and hedging counterparty credit risk.
- Credit risk transfer techniques.
- Liquidity risk and extreme credit events.
- Regulatory issues, such as Basel II and III, internal ratings systems, credit-scoring techniques and credit risk capital adequacy.
Abstracting and Indexing: Scopus; Web of Science - Social Science Index; EconLit; Excellence Research Australia; Econbiz; and Cabell’s Directory
Journal Metrics:
Journal Impact Factor: 0.880
5-Year Impact Factor: 1.045
CiteScore: 1.6
Latest papers
Understanding and predicting systemic corporate distress: a machine-learning approach
The authors construct a machine-learning-based early-warning system to predict, one year in advance, risks of systemic distress and demonstrate factors which can predict corporate distress.
Emulating the Standard Initial Margin Model: initial margin forecasting with a stochastic cross-currency basis
The authors propose a stochastic cross-currency basis model extension to resolve the impact of missing risk factors when estimating initial margin and margin valuation adjustments in cross-currency basis swaps.
Pricing default risk in stochastic time
This paper explores credit derivative pricing through the structural modeling framework and seeks to improve on how accurately such models value derivative securities.
Default forecasting based on a novel group feature selection method for imbalanced data
The authors construct a group feature selection method which combines optimal instance selection with weighted comprehensive precision in an effort to improve the performance of prediction models in relation to defaulting firms.
Benchmarking machine learning models to predict corporate bankruptcy
Based on a comprehensive sample, the authors benchmark machine learning models in the prediction of financial distress of publicly traded US firms, with gradient-boosted tress outperforming other models in one-year-ahead forecasts.
Small and medium-sized enterprises’ time to default: an analysis using an improved mixture cure model with time-varying covariates
The authors put forward a method using a support vector machine to enhance the exploration of nonlinear covariate effects if SMEs never default while also considering time-varying and fixed covariates for the incidence and latency of an event.
Instabilities in Cox proportional hazards models in credit risk
The authors explore possible instabilities in applying Cox PH models and conduct numerical studies to demonstrate the same linear specification error from APC models an occur in Cox PH estimation.
Banking on personality: psychometrics and consumer creditworthiness
This paper uses empirical methods to investigate how psychometric data can be used to augment traditional credit models.
Sovereign credit risk modeling using machine learning: a novel approach to sovereign credit risk incorporating private sector and sustainability risks
The authors investigate the effect of spillover effects from private sector risks on sovereign debt risk and the impact of rising sustainability risks on sovereign credit risk using the XGBoost classification algorithm and model interpretability…
Managerial connections and corporate risk-taking: evidence from the Great Recession
Using the the 2007-9 Great Recession as an example, the authors investigate the relationship between managers’ connections, corporate risk-taking and corporate performance during a period of crisis.
Climate-policy-relevant sectors and credit risk
This paper explores the relationship between banks exposed to climate-policy-relevant sectors and credit risk, finding that banks exposed to higher carbon emitting sectors are subject to greater credit risk than those exposed to less carbon emitting…
Dynamic class-imbalanced financial distress prediction based on case-based reasoning integrated with time weighting and resampling
The authors put forward a dynamic class-imbalanced CBR FDP model which is shown, using data from Chinese listed companies, to outperform static and dynamic CBR FDP models without resampling or time weighting.
Calibration alternatives to logistic regression and their potential for transferring the statistical dispersion of discriminatory power into uncertainties in probabilities of default
This paper compares four calibration approaches to linear logistic regression in credit risk estimation and proposes two new single-parameter families of differentiable functions as candidates for this regression.
Dynamic initial margin estimation based on quantiles of Johnson distributions
The authors compare JLSMC DIM estimates with those produced by two other methods, finding that the JLSMC algorithm is accurate and efficient, producing results comparable with nested Monte Carlo with an order of magnitude less computational effort.
Estimating correlation parameters in credit portfolio models under time-varying and nonhomogeneous default probabilities
This paper proposes new maximum likelihood estimation methods that offer greater flexibility than current methods and can account for finite portfolio sizes, scarce default data and time varying, nonhomogeneous default probabilities.
Sovereign probabilities of default in the euro area
This paper decomposes credit default swap spreads of euro area members into their risk premium and default risk elements and forecast one year probabilities of default.
Risks of long-term auto loans
The authors investigate the borrower risk factors, delinquency rates, yield curves, and interest rates of long-term auto loans.
An effective credit rating method for corporate entities using machine learning
The authors propose a new method to design credit risk rating models for corporate entities using a meta-algorithm which exploits information embedded in expert-assigned credit ratings to rank customers.
Stressing of migration matrixes for International Financial Reporting Standard 9 and Internal Capital Adequacy Assessment Process calculations
This paper demonstrates that correlation estimates are sensitive to model assumptions and estimation methodology by comparing three methods used to stress rating transition matrixes.
Generalized additive modeling of the credit risk of Korean personal bank loans
The authors demonstrate a nonlinear impact of loan and borrower characteristics when applying a GAM framework to personal loans taken from a Korean bank.
