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: 0.250
5-Year Impact Factor: 0.329
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.
The authors of this paper use power series distributions to develop a novel and flexible zero-inflated Bayesian methodology.
The authors of this paper address some issues to do with IFRS 9 and explain how to determine if an instrument has suffered serious deterioration in credit risk.
A correlated structural credit risk model with random coefficients and its Bayesian estimation using stock and credit market information
Using historical equity and credit market data, this paper illustrates the validation of a structural correlated default model applied to Black–Cox setups.
Value-at-risk bounds for multivariate heavy tailed distribution: an application to the Glosten–Jagannathan–Runkle generalized autoregressive conditional heteroscedasticity model
This paper aims to derive VaR bounds for the portfolios of possibly dependent financial assets for heavy tailed Glosten–Jagannathan–Runkle generalized autoregressive conditional heteroscedasticity processes using extreme value theory copulas.
Rating-transition-probability models and Comprehensive Capital Analysis and Review stress testing: methodologies and implementation
This paper introduces a risk component called the credit index, that represents the systematic risk part of a portfolio by a list of macroeconomic variables.
A point-in-time–through-the-cycle approach to rating assignment and probability of default calibration
This paper proposes a methodology for constructing TTC rating grades and assessing the resulting degree of PIT-ness.
Value-at-risk estimation with the Carr–Geman–Madan–Yor process: an empirical study on foreign exchange rates
This paper investigates the performance of the CGMY distribution in estimating the risk of FX rates.
Testing value-at-risk models in emerging markets during crises: a case study on South Eastern European countries
This paper examines the applicability of a wide range of VaR models in emerging markets, focusing on South Eastern European countries.