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 the following, but not limited to, topics:
- 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
Abstracting and Indexing: Scopus; Web of Science - Social Science Index; EconLit; Excellence Research Australia; and Cabell’s Directory
Impact Factor: 0.032
5-Year Impact Factor: 0.425
Addressing probationary period within a competing risks survival model for retail mortgage loss given default
This paper presents a novel approach to modeling retail mortgage LGD estimation.
This paper borrows concepts from measurement, test and psychometric theories to explore the issue of credit ratings in the Mexican corporate bond market.
In this paper, the authors analyze the credit risk of Japanese regional banks when they lend to areas outside their original operational bases.
Adapting the Basel II advanced internal-ratings-based models for International Financial Reporting Standard 9
This paper examines how we may use A-IRB models in the estimation of expected credit losses for IFRS 9 purposes.
This paper describes a simple model that can be used for risk management.
This paper proposes a portfolio credit risk model with random recovery rates.
Stochastic loss given default and exposure at default in a structural model of portfolio credit risk
The authors develop a factor-type latent variable model for portfolio credit risk that accounts for stochastically dependent probability of default (PD), loss given default (LGD) and exposure at default (EAD) at both the systematic and borrower specific…
This paper focuses on the ability of accounting ratios to predict the financial distress status of a firm as defined by the law.
The authors describe a new framework for modeling collateralized exposure under an International Swaps and Derivatives Association Master Agreement with a Credit Support Annex.
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.
This paper assesses the predictive ability of financial and nonfinancial variables for a long horizon in a large cross-sectional sample of Finnish firms
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).
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.
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.
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 authors demonstrate how different credit risk models can be efficiently implemented for scenario analysis and stress testing execution with concrete application examples.
This paper presents a rigorously motivated pricing equation for derivatives.