Journal of Risk Model Validation
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 Impact Factor: 0.250
5-Year Impact Factor: 0.325
Bayesian backtesting for counterparty risk models
Utilising Bayesian methods, the authors put forward a new means for counterparty risk model backtesting which is both simple to implement and conceptually sound.
A modified hybrid feature-selection method based on a filter and wrapper approach for credit risk forecasting
This paper proposes the chi-squared with recursive feature elimination method: a means of feature-selection which aims to improve classification performance using fewer features.
The validation of different systemic risk measurement models
The authors incorporate a capital buffer to the DebtRank model and use data from China's banking industry to compare the proposed model with others.
What can we expect from a good margin model? Observations from whole-distribution tests of risk-based initial margin models
This paper offers a means of testing initial margin models based on their predictions of the whole future distribution of returns of the relevant portfolio which is demonstrated to be more powerful than typical backtesting approaches.
Value-at-risk and the global financial crisis
The authors investigate the forecasting ability of bank VaR estimates around the 2007-9 financial crisis using daily data from seven international banks, finding systemic overstating of VaR either side of the financial crisis and mixed performance during…
Measuring the systemic importance of Chinese banks: a comparison of different risk measurement models
This paper uses DebtRank, ΔCoVaR and MES to measure the systemic importance of banks, finding that DebtRank performs best from the perspectives of size and interconnectedness.
Does the asymmetric exponential power distribution improve systemic risk measurement?
The authors use a parametric estimation for CoVaR and compare the goodness-of-fit and backtesting of AEPD with other commonly used distributions using data from the Chinese banking sector from 2008-2019.
Internet financial risk assessment in China based on a particle swarm optimization–analytic hierarchy process and fuzzy comprehensive evaluation
The authors propose a comprehensive evaluation system to index internet financial risk, based on the identification of China's internet financial risk.
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.
Performance validation of representative sample-balancing methods in loan credit-scoring scenarios
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.
Scenario design for macrofinancial stress testing
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.
Risk contagion and bank stability: the role of credit risk and liquidity risk
The authors put forward a systemic risk measurement model and measure systemic risk in China's banking sector for the period 2013-18.
Model risk in mortality-linked contingent claims pricing
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.
Model risk quantification based on relative entropy
This paper proposes a minimum relative entropy technique for challenging derivatives pricing models that can also assess the model risk of a target portfolio.
Quantifying model selection risk in macroeconomic sensitivity models
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.
Expected shortfall model based on a neural network
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.
An end-to-end deep learning approach to credit scoring using CNN + XGBoost on transaction data
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.