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
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.
This paper provides practical recommendations for the validation of risk models under the Targeted Review of Internal Models (TRIM).
In this paper, the authors contribute to the measurement of model risk by focusing on the quantification of estimation risk.
This paper investigates the effects of window-size selection on various models for value-at-risk (VaR) forecasting using high-performance computing.
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.
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.
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.
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.
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.
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).
An optimized support vector machine intelligent technique using optimized feature selection methods: evidence from Chinese credit approval data
This paper focuses on feature selection methods for support vector machine (SVM) classifiers, checking their optimality by comparing them with some statistical and baseline methods.
This paper incorporates volatility forecasting via the exponentially weighted moving average model into traditional tolerance limits for pair-trading strategies, and illustrates how the proposed method helps uncover arbitrage opportunities via the daily…
On the mathematical modeling of point-in-time and through-the-cycle probability of default estimation/ validation
In this paper, the authors focus on PD estimation and validation. They provide the mathematical modeling for both point-in-time (PIT) and through-the-cycle (TTC) PD estimation, and discuss their relationship and application in our banking system.
In this paper, the author's aim is to empirically analyze the numerical quantification of model risk, yielding exact buffers in currency amounts (for a given model uncertainty).
In this paper, the author looks at the efficacy of risk measures on energy markets and across several different stock market indexes, and calculates both the value-at-risk (VaR) and the expected shortfall (ES) on each of these data sets as well as on…
A comprehensive evaluation of value-at-risk models and a comparison of their performance in emerging markets
This paper aims to evaluate the performance of different value-at-risk (VaR) calculation methods, allowing the authors to identify models that are valid for use in emerging markets.
This paper examines the credit exposure evaluation properties of interest rate derivatives to manage counterparty credit risk, working with the real-world probability.