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 the following, but not limited to, topics:
- 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; and Cabell’s Directory
Impact Factor: 0.188
5-Year Impact Factor: 0.355
This paper develops a connection between the Hull–White parametric approach and the PCL correlation approach for CVA calculation.
This paper considers the empirical evaluation of a collective risk model with the geometric as the primary distribution and the exponential as the secondary distribution.
The author introduces the triangular approximation to the normal distribution in order to extract closed- and semi-closed-form solutions that are useful in risk measurement calculations.
Forward ordinal probability models for point-in-time probability of default term structure: methodologies and implementations for IFRS 9 expected credit loss estimation and CCAR stress testing
This paper proposes an ordinal model based on forward ordinal probabilities for rank outcomes.
This paper proposes a qualitative method to assess the maturity of model risk management practices within banks.
Asset price bubbles and the quantification of credit risk capital with sensitivity analysis, empirical implementation and an application to stress testing
This paper presents an analysis of the impact of asset price bubbles on standard credit risk measures.
In this paper, the authors present a general model of the recognition heuristic that assumes that objects’ recognition is random.
A gradient-boosting decision-tree approach for firm failure prediction: an empirical model evaluation of Chinese listed companies
In this paper, the authors employ a gradient-boosting decision-tree method to improve firm failure prediction and explain how to better analyze the relative importance of each financial variable.
Modeling impacts of stock jumps on real estate investment trust returns with application to value-at-risk
This paper aims to model the impact of extreme stock jumps on REIT returns.
Forecasting scenarios from the perspective of a reverse stress test using second-order cone programming
This paper proposes a model for forecasting scenarios from the perspective of a reverse stress test using interest rate, equity and foreign exchange data.
This paper demonstrates that the rank-order tests are unreliable for assessing models to be used to predict probabilities.
This paper focuses on the corporate stress testing models for credit risk.
Addendum to Rubtsov and Petrov (2016): “A point-in-time–through-the-cycle approach to rating assignment and probability of default calibration”
A model combination approach to developing robust models for credit risk stress testing: an application to a stressed economy
This paper uses a model combination approach to develop robust macrofinancial models for credit risk stress testing.
The authors examine the behavior of asset correlations for companies in Taiwan under the Basel Accord’s asymptotic single-risk-factor approach.
The author of this paper proposes a dynamic PD term structure model for multi-period stress testing and expected credit loss estimation.
In this paper, the authors investigate the four most commonly used risk measures – return volatility, beta, value-at-risk and stressed value-at-risk – of a TSM trading strategy.
The author of this paper proposes a prudent methodology to correct for potential biases in LGD estimations due to historical price appreciations, appraisal biases and wear-and-tear or potential damage to the house.
The authors propose a naive model to forecast ex ante value-at-risk (VaR), using a shrinkage estimator between realized volatility estimated on past return time series as well as implied volatility quoted in the market.
This paper explores the aggregation of different single ratings to a ‘consensus rating’ to get a higher precision of a debtor’s default probability. It builds upon the methodology published by Grün et al, 2013 and Lehmann and Tillich, 2016.