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:
Impact Factor: 0.485
5-Year Impact Factor: 0.429
CiteScore: 0.25
Latest papers
Old-fashioned parametric models are still the best: a comparison of value-at-risk approaches in several volatility states
The authors present backtesting results for 1% and 2.5% VaR of six indexes from emerging and developed countries using several of the best-known VaR models, including generalized autoregressive conditional heteroscedasticity (GARCH), extreme value theory…
An alternative statistical framework for credit default prediction
This study compares the gradient-boosting model with four other well-known classifiers, namely, a classification and regression tree (CART), logistic regression (LR), multivariate adaptive regression splines (MARS) and a random forest (RF).
Risk-neutral densities: advanced methods of estimating nonnormal options underlying asset prices and returns
This work expands the analysis in Cooper (1999) and Santos and Guerra (2014), and the performance of the nonstructural models in estimating the "true" RNDs was measured through a process that generates "true" RNDs that are closer to reality, due to the…
An empirical evaluation of large dynamic covariance models in portfolio value-at-risk estimation
This research develops a framework adopting conditional covariance modeling combined with various de-noising methods to estimate the portfolio VaR and proves the importance of DCC over the sample rolling method widely used in the industry.
International Financial Reporting Standard 9 expected credit loss estimation: advanced models for estimating portfolio loss and weighting scenario losses
In this paper, the authors propose a model to estimate the expected portfolio losses brought about by recession risk and a quantitative approach to determine the scenario weights. The model and approach are validated by an empirical example, where they…
Incremental value-at-risk
This paper proposes a novel method for estimating future operational risk capital: incremental value-at-risk (IVaR)
Measuring economic cycles in data
This paper determines if enough data is available for forecasting or stress testing, a better measure of data length is required.
Volatility forecasting: the role of internet search activity and implied volatility
In this study, the authors search for a benchmark model with available market-based predictors to evaluate the net contribution of internet search activity data in forecasting volatility. The paper conducts in-sample analysis and out-of-sample…
Value-at-risk in the European energy market: a comparison of parametric, historical simulation and quantile regression value-at-risk
This paper examines a set of value-at-risk (VaR) models and their ability to appropriately describe and capture price-change risk in the European energy market.
Validation of index and benchmark assignment: adequacy of capturing tail risk
This paper provides practical recommendations for the validation of risk models under the Targeted Review of Internal Models (TRIM).
Quantification of the estimation risk inherent in loss distribution approach models
In this paper, the authors contribute to the measurement of model risk by focusing on the quantification of estimation risk.
A study on window-size selection for threshold and bootstrap value-at-risk models
This paper investigates the effects of window-size selection on various models for value-at-risk (VaR) forecasting using high-performance computing.
Model risk management: from epistemology to corporate governance
In this paper, the authors conduct an analysis of model risk in an attempt to understand the main issues that lead to failures and the best way to address such issues.
Nonparametric tests for jump detection via false discovery rate control: a Monte Carlo study
The main goal of this paper is to perform a comprehensive nonparametric jump detection model comparison and validation. To this end, the authors design an extensive Monte Carlo study to compare and validate these tests.
An advanced hybrid classification technique for credit risk evaluation
In this paper, the authors employ a hybrid approach to design a practical and effective CRE model based on a deep belief network (DBN) and the K-means method.
International Financial Reporting Standard 9 expected credit loss estimation: advanced models for estimating portfolio loss and weighting scenario losses
In this paper, the authors propose a model to estimate the expected portfolio losses brought about by recession risk and a quantitative approach to determine the scenario weights.
Risk data validation under BCBS 239
Based on a survey of twenty-nine major financial institutions, this paper aims to advise banks and other financial services firms on what is needed to get ready for and become compliant with BCBS 239, especially in the area of risk data validation.
Model risk tiering: an exploration of industry practices and principles
This paper seeks to shed light on one critical area of such frameworks: model risk tiering, or the rating of risk inherent in the use of individual models, which can benefit a firm’s resource allocation and overall risk management capabilities.
Credit portfolio stress testing using transition matrixes
In this paper, the authors propose a new methodology for modeling credit transition probability matrixes (TPMs) using macroeconomic factors.
Validation of the backtesting process under the targeted review of internal models: practical recommendations for probability of default models
This paper provides practical recommendations for the validation of the backtesting process under the targeted review of internal models (TRIM).
