Validation of Risk Management Models for Financial Institutions: Theory and Practice. 2023. Edited by David Lynch, Iftekhar Hasan, and Akhtar Siddique. Cambridge University Press.
Because of their high leverage, financial institutions need to maintain a strong focus on risk modeling, both for sound firm management and as a regulatory necessity. Modeling of current and potential risks is critical to well-grounded financial decision making. Getting risk measures wrong can have dire financial consequences.
Validation of Risk Management Models for Financial Institutions, through a set of thoughtful articles, describes how effective structuring and testing of the modeling techniques used in risk management can support better financial decision making. The book does not address the question of why financial institutions may fail, which matters because financial failures and blowups continue to be accepted as part of doing business in the financial industry. This set of edited papers does, however, provide insights on how risk models are built, tested, validated, and used in a variety of financial activities. Get the models right, and a financial firm has a better chance of survival.
David Lynch, Iftekhar Hasan, and Akhtar Siddique, the editors of this book, have collected 17 papers from leading experts on issues of model validation, which they define as “the set of processes and activities intended to verify that models are performing as expected, in line with their design objectives and business uses.” These papers encompass varying levels of complexity and depth concerning the validity of model assumptions and predictions. From methodological issues to cases on specific businesses, the contributors focus on in-sample training and out-of-sample tests as validation exercises. Successful validation requires substantial data and a formal way of concluding whether a model is within an error tolerance. For financial firms, the margin for error is small. Poor testing and validation may mean the difference between financial success and firm failure.
In the first few chapters, the book centers on value at risk (VaR) modeling, the workhorse of risk models. Even with its well-known limitations and the dislike it has engendered among many traders, VaR models serve as a good foundation for risk assessments. There is no viable alternative to this backbone approach for financial institutions, but it requires extensive modeling and structural thinking to be effective. These core chapters extend modeling of the problem to the entire distribution of prices and not just a risk threshold, while also discussing the key issues of conditional backtesting and benchmarking for the ongoing monitoring of risks.
Of course, one of the existential risks over the last decade has been the COVID-19 pandemic. Research points to the failure of VaR models to react quickly enough in the spring of 2020. There is reason to hope, however, that future outlier events can be addressed more effectively by including past data extremes in the analysis. Unfortunately, as clearly enunciated in this book, the fundamental stress-testing problem in regard to extreme events is that we simply do not have enough stress periods to train risk models properly.
Several chapters, representing more than half the book, focus on credit risk modeling by discussing issues of counterparty risk, retail credit models, and wholesale banking of large loans. Here, there is a focus not just on market price dynamics but also on allowance for loss. Proper modeling of the probability of loss and loss given default is critical to measuring risks, especially given the currently high growth in private credit funds.
While VaR modeling has dominated trading businesses, credit default modeling may be more critical for firm risk, given the increased difficulty of hedging these events. Again, with a limited number of recessions and unique credit events, the measurement and validation of loss assumptions are not easy issues to address. The goodness of fit for any model must be balanced against the adequacy of the sample data. Contributors to this volume present the problems associated with credit management both analytically and through a case study.
Examining trading and lending business risk is critical, but there is also a need to roll risk up to the enterprise level, a key topic when thinking about firm risk. Models must also be balanced against operational risk and the demands of supervisory stress testing by regulators. All these issues are addressed in various chapters, but the common drawback of any edited book of research papers is present: The papers have varying quality and complexity, and the integration of topics does not always flow effectively for the reader who desires a sequentially organized review of the essential topics.
Unfortunately, model construction and validation often do no more than fight the last battle on losses or address the desires of regulators. The process does not prepare institutions for black swans, tail events, or the consequences of making the wrong decisions. While not the focus of model validation, dealing with “unknown unknowns,” extreme scenarios, and unique risk events is fundamental to improved risk decision making. In a complex financial world, diversification and leverage are key components of risk management that influence the effectiveness of validation. Validating on the basis of past data is the best this book has to offer for building models, yet addressing uncertainty, ambiguity, and the complexity of markets is necessary for any useful risk discussion.
With its focus on model validation, the book deals with a narrowly specialized topic. Nevertheless, any reader involved in investment management or financial institutions will find it useful for generating keener insights into building and interpreting risk models. Losses at money managers and hedge funds, like the faltering of financial institutions, are often associated with risk model failure in the form of giving incorrect or ambiguous answers or focusing on the wrong risks. Reading this book is not going to prevent bad decisions or constrain inappropriate risk taking, but it will improve model building, which is foundational for minimizing losses.
Many potential readers of Validation of Risk Management Models for Financial Institutions may not be focused on managing financial risk, but gaining a deeper understanding of model validation is helpful for anyone working in the investment field. Models are useful only if fully tested and validated. We need to know their limitations, and this book provides a valuable guide to the critical issues faced when using risk models.
If you liked this post, don’t forget to subscribe to the Enterprising Investor.
All posts are the opinion of the author. As such, they should not be construed as investment advice, nor do the opinions expressed necessarily reflect the views of CFA Institute or the author’s employer.
Professional Learning for CFA Institute Members
CFA Institute members are empowered to self-determine and self-report professional learning (PL) credits earned, including content on Enterprising Investor. Members can record credits easily using their online PL tracker.