Risk Model Validation (3rd edition)
First published:
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The concepts of model risk and validation
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
Chapter 3 presented a number of ways in which a QRM might fail. However, these models are meant to be useful tools for risk control and management. Therefore, methods have to be found that can detect and correct, or at least manage, model failure and model imperfection. In this regard, two terms have become prominent: “model risk”, which describes the possibility of QRM failure and its potential consequences, and “validation” as a main weapon against model risk. This chapter will show how to define, and what to expect and not to expect from, these two concepts.
It is not easy to define what model risk and validation are. However, there is a longer tradition of models and of model validation in the natural sciences and engineering, which is a good starting point. Models in social science are usually of a very different kind, while models in finance (and QRMs in particular) lie somewhere between these two extremes.
MODEL VALIDATION AND MODEL UNCERTAINTY IN NATURAL SCIENCES AND ENGINEERING
The rapid increase in computer power since the 1980s has been accompanied by an increasing use of computerised models in the natural sciences and engineering. Model developers and users
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