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: 0.4
5-Year Impact Factor: 0.4
CiteScore: 0.5
Latest papers
Research on the multifractal volatility of Chinese banks based on the synthetic minority oversampling technique, edited nearest neighbors and long short-term memory
The authors propose the SMOTEENN-LSTM method to predict risk warnings for Chinese banks, demonstrating the improved performance of their model relative to commonly used methods.
A model combining Optuna and the light gradient-boosting machine algorithm for credit default forecasting
The authors put forward a default prediction model designed to make the analysis of complex, highly dimensional and imbalanced real-world bank data easier.
Litigation risk assessment: a novel quantitative recency–frequency–monetary model
The authors assess litigation risk and credit risk of companies and investigate interrelationships between these risks, finding a correlation between them.
Analyzing credit risk model problems through natural language processing-based clustering and machine learning: insights from validation reports
The authors use clustering and machine learning techniques to analyze validation reports, providing insights to the development, implementation and maintenance of credit risk models.
Machine learning prediction of loss given default in government-sponsored enterprise residential mortgages
The authors apply machine learning techniques to Loss Given Default estimation, identifying key variables in LGD prediction and evaluating the performance of various models.
Forecasting India’s foreign trade dynamics: evaluation of alternative forecasting models in the post-pandemic period
The authors aim to determine how India's foreign trade will change following Covid-19 and the Russia-Ukraine conflict, comparing several forecasting models and identifying that which performs best.
The impact of deterioration in rating-model discriminatory power on expected losses
The authors propose a means to estimate the effects on a portfolio’s expected credit loss created by underwriting model risks.
A study of China’s financial market risks in the context of Covid-19, based on a rolling generalized autoregressive score model using the asymmetric Laplace distribution
The authors construct a risk measurement model for the financial market during the Covid-19 pandemic, using data from the Shanghai Stock Exchange for empirical analysis.
Financial distress prediction with optimal decision trees based on the optimal sampling probability
The authors propose and validate a tree-based ensemble model for financial distress prediction which is demonstrated to outperform comparative models.
Quantifying credit portfolio sensitivity to asset correlations with interpretable generative neural networks
This study introduces a method for assessing the impact of asset correlations on credit portfolio value-at-risk using variational autoencoders (VAEs), offering a more interpretable approach than previous methods and improving model interpretability.
Default prediction based on a locally weighted dynamic ensemble model for imbalanced data
The authors put forward a locally weighted dynamic ensemble model which can predict financial institutions' default statues five years ahed.
Shapley values as an interpretability technique in credit scoring
The authors analyze the usefulness of the Shapley value as a machine learning interpretability technique in credit scoring.
Online attention and directors’ and officers’ liability insurance: evidence from Chinese listed firms
The authors investigate how online attention impacts the purchases of D&O liability insurance.
Forecasting the default risk of Chinese listed companies using a gradient-boosted decision tree based on the undersampling technique
The authors put forward a model for default prediction designed to minimise the impact of imbalanced classification, verifying its effectiveness with real world data from Chinese listed companies.
Exchange rate risk management for contractors within a hybrid payment scheme: a case study in Punta del Este, Uruguay
The author proposes methods for how contractors may attempt to mitigate exchange rate risks in hybrid payment systems and validates these with empirical data from a hypothetical project.
A new automated model validation tool for financial institutions
The authors put forward a novel automated validation tool, based on US Federal Reserve and Office of the Comptroller of the Currency regulatory guidance, which is used to to validate predictive models for financial organizations.
Overfitting in portfolio optimization
The authors measure the performance of sample-based rolling-window neural network (NN) portfolio optimization strategies and demonstrate that correctly set up NN-based strategies can outperform the 1/N strategy.
On the mitigation of valuation uncertainty risk: the importance of a robust proxy for the “cumulative state of market incompleteness”
The author put forwards a means to mitigate asset risk and valuation uncertainty risk which relies on investors conditioning valuations of new assets on a dynamically evolving intertemporal mechanism
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.
