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
Current expected credit loss procyclicality: it depends on the model
This work looks at a wide range of models to test the degree to which CECL is procyclical for different types of model.
A sensitivity analysis of the alpha factor
In this paper, we investigate the alpha factor’s sensitivity to key model parameters under stylized portfolio assumptions in order to better understand its complex characteristics. Our analysis is based on the numerical simulation of alpha sensitivities…
Contagious defaults in a credit portfolio: a Bayesian network approach
Costs of capital under credit risk
In cost-of-capital computations, credit risk is only taken into consideration at the level of the debt beta approach. We show that applications of the debt beta approach in company valuation suffer from unrealistic assumptions about the market index and…
Basel risk weight functions and forward-looking expected credit losses
The authors establish that the combination of lifetime ECL and the Basel Capital Adequacy Framework, which relies on a one-year horizon, results in capital overestimation. Alongside this finding, and in order to alleviate the problem, they propose two…
Credit valuation adjustment wrong-way risk in a Gaussian copula model
In this paper, we present an analytical expression for CVA with WWR under the assumption of the lognormally distributed trade value.
On probability of default and its relation to observed default frequency and a common factor
This paper considers a definition of through-the-cycle as independent from an economic state that can result in a time-varying TTC probability of default.
Asset correlation estimation for inhomogeneous exposure pools
This study investigates the systematic error that is made if the exposure pool underlying a default time series is assumed to be homogeneous when in reality it is not.
An efficient portfolio loss model
This paper develops a parsimonious model for evaluating portfolio credit derivatives dependent on aggregate loss.
A consumer credit risk structural model based on affordability: balance at risk
A statistical technique to enhance application scorecard monitoring
Application scoring plays a critical role in determining the future quality of a lender’s book. It is therefore important to monitor the performance of an application scorecard to ensure it performs as expected.
Wrong-way risk of interest rate instruments
This paper investigates wrong-way risk effects on the pricing of counterparty credit risk for interest rate instruments.
The influence of firm efficiency on agency credit ratings
Calibration and mapping of credit scores by riding the cumulative accuracy profile
Are lenders using risk-based pricing in the Italian consumer loan market? The effect of the 2008 crisis
This paper analyzes whether in Italy the price of consumer loans is based on borrower-specific credit risk.
Systemic risk in the financial system: capital shortfalls under Brexit, the US elections and the Italian referendum
This paper uses SRISK to quantify the estimated capital shortfalls of financial institutions under three relevant stress events that occurred in 2016: Brexit, the Trump election and the Italian referendum.
A fifty-year retrospective on credit risk models, the Altman Z-score family of models and their applications to financial markets and managerial strategies
This paper reflects upon the evolution of the Altman family of bankruptcy prediction models, as well as their extensions and multiple applications in financial markets and managerial decision making.
Bank risk, bank bailouts and sovereign capacity during a financial crisis: a cross-country analysis
This paper analyzes the competitive effects of government bailout expectations on bank risk using a sample of banks in OECD countries from 2005 to 2015.
Calculating capital charges for sector concentration risk
This paper proposes a methodology to quantify capital charges for concentration risk when economic capital calculations are conducted within a multifactor Merton framework.
An empirical study on credit risk management: the case of nonbanking financial companies
The aim of this paper is to predict future default behaviors of nonbank financial company customers using credit scores.