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
Creditwatches and their impact on financial markets
Benchmarking the loss given default parameter for mortgage loan portfolios under stress
The authors analyze the impact of a decline in property prices that leads to stressed recovery rates for collateral on the loss given default (LGD) parameter in portfolios of mortgage loan.
Financial and nonfinancial variables as long-horizon predictors of bankruptcy
This paper assesses the predictive ability of financial and nonfinancial variables for a long horizon in a large cross-sectional sample of Finnish firms
Further investigation of parametric loss given default modeling
The authors conduct a comprehensive study of some parametric models that are designed to fit the unusual bounded and bimodal distribution of loss given default (LGD).
Modeling the current loan-to-value structure of mortgage pools without loan-specific data
This paper presents a method for approximating the current loan-to-value (CLTV) and remaining principal structures of heterogeneous mortgage loan pools.
Modeling corporate customers’ credit risk considering the ensemble approaches in multiclass classification: evidence from Iranian corporate credits
This paper introduces a model which enables lenders to develop specific policies for credit granting by predicting the solvency and insolvency rates of their corporate clients.
Modeling joint defaults in correlation-sensitive instruments
This paper presents a simple model for joint defaults and shows how it can be applied to pricing and risk-managing instruments that are sensitive to credit correlation.
Estimating credit risk parameters using ensemble learning methods: an empirical study on loss given default
This study investigates two well-established ensemble learning methods: Stochastic Gradient Boosting and Random Forest, and proposed two new ensembles.
The impact of loan-to-value on the default rate of residential mortgage-backed securities
This paper analyzes the validity of using the loan-to-value (LTV) ratio to explain the behavior of mortgage borrowers at an empirical level.
The application of credit risk models to macroeconomic scenario analysis and stress testing
The authors demonstrate how different credit risk models can be efficiently implemented for scenario analysis and stress testing execution with concrete application examples.
A bond consistent derivative fair value
This paper presents a rigorously motivated pricing equation for derivatives.
The double default value-of-the-firm model
This paper analyses whether the double default treatment under Basel II is appropriate to capture the asymmetric relationship between an obligor and its guarantor.
Fitting a distribution to value-at-risk and expected shortfall, with an application to covered bonds
This paper suggests simple and intuitive models for covered bonds that allow quantitative assessment of expected loss and the impact of asset encumbrance.
A framework for market, credit and transfer risk aggregation and stress testing
The authors develop a framework that consistently and fully integrates the market, credit and country transfer risks of a general portfolio of financial assets in a multi-period setup.
Contingent credit default swaps: accurate and approximate pricing
This paper analyzes the pricing of contingent credit default swaps.
Market pricing of credit linked notes: the influence of the financial crisis
This paper analyzes whether the financial crisis of 2007–9 had an effect on the mispricing of CLNs.
A credit portfolio framework under dependent risk parameters: probability of default, loss given default and exposure at default
This paper introduces a credit portfolio framework that allows for dependencies between default probabilities, secured and unsecured recovery rates and exposures at default (EADs).
Are all collections equal? The case of medical debt
This paper examines the predictive value of medical collections in assessing consumer creditworthiness with credit scoring models.
Default risk of money-market fund portfolios
This paper proposes a semi-analytic approach to quantify the default risk associated with Money-Market Fund (MMF) portfolios.
Loss distributions: computational efficiency in an extended framework
This paper contributes to the literature for mixture models by leveraging an efficient algorithm for computing the density function of the loss distribution and extending the model in two key areas: constructing the systemic variable from a continuous…