Coherent Stress Testing: A Bayesian Approach to the Analysis of Financial Stress

by Riccardo Rebonato

In Coherent Stress Testing: A Bayesian Approach , industry expert Riccardo Rebonato presents a groundbreaking new approach to this important but often undervalued part of the risk management toolkit. Based on the author's extensive work, research and presentations in the area, the book fills a gap in quantitative risk management by introducing a new and very intuitively appealing approach to stress testing based on expert judgement and Bayesian networks. It constitutes a radical departure from the traditional statistical methodologies based on Economic Capital or Extreme-Value-Theory approaches. The book is split into four parts. Part I looks at stress testing and at its role in modern risk management. It discusses the distinctions between risk and uncertainty, the different types of probability that are used in risk management today and for which tasks they are best used. Stress testing is positioned as a bridge between the statistical areas where VaR can be effective and the domain of total Keynesian uncertainty. Part II lays down the quantitative foundations for the concepts described in the rest of the book. Part III takes readers through the application of the tools discussed in part II, and introduces two different systematic approaches to obtaining a coherent stress testing output that can satisfy the needs of industry users and regulators. In part IV the author addresses more practical questions such as embedding the suggestions of the book into a viable governance structure.

240 pages, Hardcover

First published May 21, 2010

Reviews

[Nick Klagge · Rating 4 out of 5]

I read this one "because of" work although not "for" work. I won't bore my dear Goodreads friends with technical nuances, but I thought the book was quite interesting. It addresses head-on a conundrum that is at the heart of stress-testing: the longer your stress time-horizon, the fewer relevant datapoints you can draw on, and the further you go back in history for your dataset, the less sure you can be that those datapoints represent "the same thing" you are interested in today. Briefly, Rebonato's answer to this conundrum is to develop a framework that leans heavily on human judgment, but in a formal and transparent way. He accomplishes this via a nice tool that I had never encountered before, called a Bayesian net (sounds more complicated than it is). This is very different from the standard approach to stress testing, and I think it is really valuable to have people developing a diverse set of ideas about how one might go about the process.