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 Impact Factor: 0.250
5-Year Impact Factor: 0.325
Forecasting the loss given default of bank loans with a hybrid multilayer LGD model by extending multidimensional signals
The authors employ signaling theory and machine learning methods to investigate loss given default predictions of commercial banks and propose a method to improve the accuracy of these predictions.
The authors validate 12 of the most representative sample-balancing methods used for credit-scoring models, finding that a combined SMOTE and Editor Nearest Neighbor method is optimal.
The author presents an empirical approach to scenario design for selecting a stress scenario for international macrofinancial variables and compares this approach with a historical scenario approach.
The authors put forward a systemic risk measurement model and measure systemic risk in China's banking sector for the period 2013-18.
The authors investigate the influence of model risk on pricing life products and demonstrate that classical Lee-Carter-type models can be less accurate than the proposed model.
Quantification of model risk with an application to probability of default estimation and stress testing for a large corporate portfolio
This paper discusses the building of obligor-level rather than segment-level hazard rate corporate probability of default models for stress testing.
This paper proposes a minimum relative entropy technique for challenging derivatives pricing models that can also assess the model risk of a target portfolio.
The authors compare forecasts and uncertainties of three possibilities in model selection: the model selected as best, the best ensemble and the model not selected.
General bounds on the area under the receiver operating characteristic curve and other performance measures when only a single sensitivity and specificity point is known
Using a single true positive - true negative pair, the author shows how to calculate the area under a ROC curve.
This paper presents a model that combines ES models based on EVT and neural networks and meets all criteria for the validity of the Basel III standard.
The authors find that machine learning methods can generate satisfactorily performing credit score models based on data from the 90-days prior to the score date, where traditional models can perform poorly.
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.