Risk Model Validation (3rd edition)
First published:
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Impact of machine learning, outlook and conclusions
Introduction
Basics of quantitative risk models
Usage of statistics in quantitative risk models
How can a risk model fail?
The concepts of model risk and validation
Model risk frameworks
Validation tools
Regulation
Stylised facts and classical approaches
Benchmarking with machine learning
Extending the risk horizon
Modelling and simulation
Data
Model results
Impact of machine learning, outlook and conclusions
References
“As complexity rises, precise statements lose meaning and meaningful statements lose precision.” Lotfi A. Zadeh, as paraphrased by McNeill and Freiberger (1993)
The above quotation from Zadeh, the inventor of fuzzy logic and perhaps one of the most cited authors in applied mathematics and computer science, very well characterises the situation faced in risk modelling.
On the one hand, QRMs are designed to supposedly reflect, to very high accuracy and precision, the impact of given scenarios on the portfolio or business model. It may sometimes be hard to resist the temptation to micro-model all the details that come to mind when thinking about potential future scenarios and their impact on the quantity of interest – eg, the P&L of the portfolio, or the profitability of the business model. This temptation may be motivated by recent history, either in the form of a severe crisis experienced (“in the future, we need to avoid the supposed causes of the crisis”) or in the form of a benign part of the business cycle recently gone through (“this time is different, the trend is sustainable”).
On the other hand, we intuitively feel that the developments we are going to experience
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