Risk Model Validation (3rd edition)
First published:
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Benchmarking with machine learning
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
Empirical data indicate that financial markets feature several so-called stylised facts, such as changing volatilities and fat tails of return distributions. As a first step to address the aspect of changing volatility, filtered historical simulation was introduced at the end of the previous chapter. This method captures time-varying characteristics of markets by starting with the dynamics of individual risk factors. In this sense, it provides a bottom-up perspective.
In this chapter, a benchmark model for market risk will be presented that is inherently of a top-down flavour. The starting point will be the time series of profits and losses at a portfolio level, a dataset that every bank using internal models already has in stock. That implies several advantages:
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the benchmark model is easy to implement with existing risk models;
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since the benchmark model provides a top-down approach, it is a useful companion to other risk model validation processes; and
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the benchmark model can focus on the distributional properties of the portfolio profit and loss in contrast to the distributional properties of risk factors.
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The aim of this
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