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: 0.4
5-Year Impact Factor: 0.6
CiteScore: 1.6
Latest papers
Soft information in financial distress prediction: evidence of textual features in annual reports from Chinese listed companies
The authors use textual data in a model to predict financial distress, demonstrating that this can enhance prediction outcome versus traditional financial data alone.
Distributionally robust optimization approaches to credit risk management of corporate loan portfolios
A new approach to manage credit risk in financial institutions - the empirical divergence-based distributionally robust optimization - is proposed and shown to alleviate the challenges of sample sparsity and data uncertainty in credit risk modeling.
A method of classifying imbalanced credit data based on the AC-CTGAN hybrid sampling algorithm
The authors put forward a novel method with which to identify risk in consumer credit data and demonstrate its enhanced generalization ability compared to commonly used methods.
Consumer credit card payment dynamics over the economic cycle
This papers uses data from 1.8 million credit card accounts to investigate how consumers revolve credit card debt and the impact of this on default risk.
Credit portfolio modeling and pricing using the Poisson binomial distribution
The authors extend the Poisson binomial distribution by integrating correlation and dependence between events, improving model validation and the capture of complex events.
Random survival forests and Cox regression in loss given default estimation
The authors put forward a loss given default model which incorporates the survival process and illustrate their approach with real mortgage data.
How do credit rating agencies and bond investors react to credit guarantees? Evidence from China’s municipal corporate bond market
This paper investigates China's municipal corporate bond market, examining the responses of credit rating agencies and bond investors to credit guarantees.
Credit risk management: a systematic literature review and bibliometric analysis
The authors undertake a literature review and bibliometric analysis of 774 credit risk research papers.
Characteristics of student loan credit recovery: evidence from a micro-level data set
The authors investigate delinquent student loans, identifying factors which influence the likelihood of recovery and proposing means to improve student loan credit recovery rates.
Credit contagion risk in German auto loans
The authors employ a data set of over 5 million German auto loans to investigate credit contagion risk and show that defaults cannot be attributed to single factors.
Tail sensitivity of stocks to carbon risk: a sectoral analysis
The authors investigate the tail sensitivity of US industry returns in relation to changes of carbon-driven climate risk, finding that tail sensitivities rise with the greenhouse has emissions of an industry.
Nonbanking financial institutions and sustainability issues: empirical evidence on the impact of environmental, social and governance scores on market performance
The authors investigate relationships between environmental, social and governance scores and market-to-book ratios using data from North American and European nonbanking financial institutions.
The role of a green factor in stock prices: when Fama and French go green
The authors propose a means to capture climate change risk exposure by combining a green factor with typical frameworks used for explaining stock returns.
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.
