Celebrating the 10th Anniversary of The Journal of Risk Model Validation

Dr Harald Scheule, Associate Professor of Finance at University of Technology Sydney, presents a virtual special issue looking back at the past ten years of risk model validation.

I am delighted to present this special edition for a special occasion: The Journal of Risk Model Validation has its 10th anniversary and it is time to reflect and honour the terrific research output presented over this term. We are very pleased by the impact on industry and academia alike. Many financial institutions are subscribers and risk modellers, risk executives, consultants alike have taken the findings on board to build better - often more accurate and robust - risk models.

Today we present you with some of our most popular papers from the journal, stemming from the last nine years. The Journal of Risk Model Validation articles are widely cited and compete at the highest level in related disciplines such as econometrics, finance, mathematics, and operations research. We have chosen the papers for the special edition based on the citation counts provided by Google Scholar for a given publication year as an impact measure. The papers develop new risk model methodologies and compare the model performance across various information sets and existing risk models. The work covers all areas of financial risk models: credit risk, market risk, operational risk liquidity risk and commodity risk. New risk model validation methodologies are developed and presented. In particular, the articles analyse the performance of models for economic risks, including commodity, credit, liquid, market and operational risk. They advance risk measures beyond value at risk and expected shortfall and provide guidance to meet regulatory expectations. Please allow me to showcase some of the recent advances in our discipline that were the basis of this research.

Post GFC, prudential regulators have increased risk model requirements and rigorous standards are being implemented globally. The building of forward looking early warning models has become a commodity. These models are exposed to stress tests often based on adverse economic scenarios or stressed frailty effects that capture unexplained economic risk. Furthermore, regulators have greatest concerns about the consistency of risk models and comparative model building and benchmarking to challenger models and competing risk models are common.

Risk model methodologies have advanced in terms of their time focus. Much of the original work was based in science where experiments abstract from business cycles and are often applied within ‘laboratory environments’ which ensure that the experiment is repetitive. Unfortunately economic risk models are empirical and rely on historical data. It takes financial crises such as the Global financial Crisis in 2007-2009 to gather information and learn about economic properties.

Today, research in risk model building and validation takes into account the economic fundamentals of the data generating processes. For example, it is now common to include the life cycle of financial products from origination to payoff, default or maturity while controlling for the current state of the economy. Another aspect is the efficient analysis of available information that includes Bayesian modelling, non-parametric modelling and frailty modelling. Risk models are extended to exploit observable and unobservable information in the most efficient ways. All empirical risk models remain to be subject to model risk as we continue to rely on assumptions and the historic data that we observe. Much work has gone into the measurement and the interpretation of model risk.

The past nine years have been exciting years and few disciplines have grown at the same speed as our discipline. Private and public institutions have invested unprecedented resources into a better understanding of risk models. The Journal of Risk Model Validation is proud to be part of this important process. Our research has made an impact on how risk models are built and validated in practice

This process will undoubtedly continue and I would like to invite you to share your findings with our community. The Journal of Risk Model Validation is a great platform to achieve this. I hope you enjoy reading this special issue. I would like to thank all readers, authors and JRMV team for their amazing contributions and support during the past decade.

Papers in this issue

Stress-testing credit risk parameters: an application to retail loan portfolios
Daniel Rösch and Harald Scheule
The Journal of Risk Model Validation, 2007, Volume 1(1);55-75

A framework for stress testing banks’ credit risk
Jim Hock-Yuen Wong, Ka-Fai Choi and Pak-Wing Fong
The Journal of Risk Model Validation, 2008, Volume 2(1);3-23

Risk contributions, information and reverse stress testing
Jimmy Skoglund and Wei Chen
The Journal of Risk Model Validation, 2009, Volume 3(2): 61-77

Effective modeling of wrong way risk, counterparty credit risk capital, and alpha in Basel II
Juan Carlos Garcia Cespedes, Juan Antonio de Juan Herrero, Dan Rosen and David Saunders
The Journal of Risk Model Validation, 2010, Volume 4(1); 71-98

Reverse stress tests with bottom-up approaches
Peter Grundke
The Journal of Risk Model Validation, 2011, Volume 5(1); 71-90

A methodology for point-in-time-through-the-cycle probability of default decomposition in risk classification systems
Magnus Carlehed and Alexander Petrov
The Journal of Risk Model Validation, 2012 Volume 6(3):3-25

Assessing the performance of generalized autoregressive conditional heteroskedasticity-based value-at-risk models: a case of frontier markets
Dany Ng Cheong Vee, Preethee Nunkoo Gonpot and Noor Sookia
The Journal of Risk Model Validation, 2012, Volume 6(4); 95-111

Individual and flexible expected shortfall backtesting
Marcelo Brutti Righi and Paulo Sergio Ceretta
The Journal of Risk Model Valdiation, 2013, Volume7(3); 3-20

A proposed framework for backtesting loss given default models
Gert Loterman, Michiel Debruyne, Karlien Vanden Branden, Tony Van Gestel, Christophe Mues
The Journal of Risk Model Validation, 2014, Volume 8(1):69-90

The role of the loss function in value-at-risk comparisons
Pilar Abad, Sonia Benito Muela and Carmen López Martín
The Journal of Risk Model Validation, 2015, Volume 9(1); 1-19