Journal of Risk Model Validation
ISSN:
1753-9579 (print)
1753-9587 (online)
Editor-in-chief: Steve Satchell
About this journal
As monetary institutions rely greatly on economic and financial models for a wide array of applications, model validation has become progressively inventive within the field of risk. The Journal of Risk Model Validation focuses on the implementation and validation of risk models, and aims to provide a greater understanding of key issues including the empirical evaluation of existing models, pitfalls in model validation and the development of new methods. We also publish papers on back-testing. Our main field of application is in credit risk modelling but we are happy to consider any issues of risk model validation for any financial asset class.
The Journal of Risk Model Validation considers submissions in the form of research papers on topics including, but not limited to:
- Empirical model evaluation studies
- Backtesting studies
- Stress-testing studies
- New methods of model validation/backtesting/stress-testing
- Best practices in model development, deployment, production and maintenance
- Pitfalls in model validation techniques (all types of risk, forecasting, pricing and rating)
Abstracting and Indexing: Scopus; Web of Science - Social Science Index; EconLit; Econbiz; and Cabell’s Directory
Journal Metrics:
Journal Impact Factor: 0.250
5-Year Impact Factor: 0.325
CiteScore: 0.5
Latest papers
Evaluation of backtesting techniques on risk models with different horizons
In this study different value-at-risk (VaR) models are analyzed under different estimation approaches (filtered historical simulation, extreme value theory and Monte Carlo simulation) and backtested with different techniques.
Backtesting of a probability of default model in the point-in-time–through-the-cycle context
This paper presents a backtesting framework for a probability of default model, assuming that the latter is calibrated to both point-in-time and through-the-cycle levels.
A prudent loss given default estimation for mortgages. II
This paper introduces a prudent methodology to accurately estimates loss given default for mortgage portfolios and to stress test those portfolios effectively.
A pricing model with dynamic credit rating transition matrixes
This paper incorporates a stochastic credit rating transition matrix into the Acharya–Das–Sundaram model and implements a simulation based pricing method
The value-at-risk of time-series momentum and contrarian trading strategies
This paper not only provides a theoretical model for the value-at-risk of active and passive trading strategies but also discusses the substantial implications relevant to risk management.
Comprehensive Capital Analysis and Review consistent yield curve stress testing: from Nelson–Siegel to machine learning
This paper develops different techniques for interpreting yield curve scenarios generated from the FRB’s annual CCAR review.
Validation nightmare: the slotting approach under International Financial Reporting Standard 9
This paper makes an important contribution to the practice of validation by focusing on an under-researched area of the slotting approach to real estate specialized lending under the International Financial Reporting Standard 9 (IFRS 9) framework.
Nonconvex noncash risk measures
This paper looks at nonconvex, noncash risk measures with p-norm (1 ≤ p ≤ ∞) for nonweak cone-type acceptable sets.
What can we learn from what a machine has learned? Interpreting credit risk machine learning models
This paper studies a few popular machine learning models using LendingClub loan data, and judges these on performance and interpretability
Empirical validation of the credit rating migration model for estimating the migration boundary
In this paper, a structural model for credit rating migration is developed and validated, by which the migration boundary is recovered for the first time.
Research on listed companies’ credit ratings, considering classification performance and interpretability
This study uses the correlation coefficient and F-test to select the initial features of a credit evaluation system, and then a validity index for a second selection to ensure that the feature system has the optimum ability to discriminate in determining…
Beyond the contract: client behavior from origination to default as the new set of the loss given default risk drivers
In this paper, we expand the modeling process by constructing a set of client-behavior-based predictors that can be used to construct more precise models, and we investigate the economic justifications empirically to examine their potential usage.
Bifractal receiver operating characteristic curves: a formula for generating receiver operating characteristic curves in credit-scoring contexts
This paper formulates a mathematical model for generating receiver operating characteristic (ROC) curves without underlying data.
A verification model to capture option risk and hedging based on a modified underlying beta
This paper analyzes the relationship between option risk and expected return from the perspective of the underlying beta, and estimates the degree of correlation.
A hybrid model for credit risk assessment: empirical validation by real-world credit data
This paper examines which hybridization strategy is more suitable for credit risk assessment in the dynamic financial world.
Determination of weights for an optimal credit rating model based on default and nondefault distance maximization
This study proposes a credit rating model that accurately identifies default and nondefault companies by maximizing intergroup credit score deviations and minimizing intragroup deviations.
How accurate is the accuracy ratio in credit risk model validation?
The author presents four methods to estimate the sample variance of the accuracy ratio and the area under the curve.
Statistical properties of the population stability index
This paper aims to fill a gap in the literature by providing statistical properties of the population stability index (PSI) and some recommendations on its use.
A FAVAR modeling approach to credit risk stress testing and its application to the Hong Kong banking industry
In this paper, a credit risk stress testing model based on the factor-augmented vector autoregressive (FAVAR) approach is proposed to project credit risk loss under stressed scenarios.
Benchmarking loss given default discount rates
This paper provides a theoretical and empirical analysis of alternative discount rate concepts for computing loss given default rates using historical bank workout data.