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
Impact Factor: 0.485
5-Year Impact Factor: 0.429
Can we take the “stress” out of stress testing? Applications of generalized structural equation modeling to consumer finance
This paper provides a practical introduction to the GSEM statistical framework in risk management, and it illustrates the game-changing potential of this methodology with two empirical applications.
In this paper a simple approach for including central bank and government intervention in credit models is developed and illustrated using the Fed’s data for the CCAR 2021 stress test.
The importance of window size: a study on the required window size for optimal-quality market risk models
In this paper the authors study different moving-window lengths for value-at-risk evaluation, and also address subjectivity in choosing the window size by testing change point detection algorithms.
In this paper the authors propose a semi-parametric, parsimonious value-at-risk forecasting model based on quantile regression and readily available market prices of option contracts from the over-the-counter foreign exchange interbank market.
Predicting financial distress of Chinese listed companies using a novel hybrid model framework with an imbalanced-data perspective
In this paper a novel hybrid model framework is constructed to solve the problem of predicting the financial distress of Chinese listed companies using imbalanced data.
Calibration of rating grades to point-in-time and through-the-cycle levels of probability of default
The paper argues for the need for and importance of the dual calibration of a probability of default (PD) model (ie, calibration to both point-in-time and through-the-cycle PD levels.)
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.
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.
This paper introduces a prudent methodology to accurately estimates loss given default for mortgage portfolios and to stress test those portfolios effectively.
This paper incorporates a stochastic credit rating transition matrix into the Acharya–Das–Sundaram model and implements a simulation based pricing method
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.
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.
This paper looks at nonconvex, noncash risk measures with p-norm (1 ≤ p ≤ ∞) for nonweak cone-type acceptable sets.
This paper studies a few popular machine learning models using LendingClub loan data, and judges these on performance and interpretability
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.