Journal of Risk Model Validation

Aims & Scope

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)

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	0.6
2024	0.40

SJR (SCImago Journal Rank)

Year	Value
2024	0.139

Quartile

Year	Value
2024	Q4

h-index

Year	Value
2024	10

Journal Rank

Year	Value
2024	25430

Journal Citation Indicator

Year	Value
2024	27

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Economics, Econometrics and Finance and Mathematics, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Benchmarking default prediction models: pitfalls and remedies in model validation

  Citation: 60

  Authors: Roger

• Loss given default modeling: a comparative analysis

  Citation: 32

  Authors: Olga, Yuri

• Reverse stress tests with bottom-up approaches

  Citation: 26

  Authors: Peter

• Backtesting general spectral risk measures with application to expected shortfall

  Citation: 25

  Authors: Nick, Mike

• Backtesting VaR models:a two-stage procedure

  Citation: 24

  Authors: Timotheos, Stavros

• Effective modeling of wrong way risk, counterparty credit risk capital and alpha in Basel II

  Citation: 24

  Authors: Juan Carlos, Juan Antonio, Dan, David

• Calibrating low-default portfolios, using the cumulative accuracy profile

  Citation: 21

  Authors: Marco

• Individual and flexible expected shortfall backtesting

  Citation: 21

  Authors: Marcelo Brutti, Paulo Sergio

• Liquidity effects on value-at-risk limits: construction of a new VaR model

  Citation: 19

  Authors: Sunny B., Jan W.

• A methodology for point-in-time–through-the-cycle probability of default decomposition in risk classification systems

  Citation: 18

  Authors: Magnus, Alexander

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