Risk Model Validation (3rd edition)
First published:
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Extending the risk horizon
Extending the risk horizon
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 Christoffersen, Diebold and Schuermann (1998) have pointed out, there is no single “all-purpose” time horizon for risk management and risk measurement. Several aspects may influence the choice of the relevant horizon:
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regulatory capital for market risk is usually computed on a 10-day horizon, whereas Basel’s Pillar 2/ICAAP approaches mostly use horizons such as one year and beyond;
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risk horizons vary by asset class – eg, a listed equity may be associated with a one-day horizon, whereas a corporate bond or an emerging market sovereign bond is more likely to have a longer horizon; and
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risk horizons may also vary from industry to industry (eg, banking versus insurance versus asset management).
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A key ingredient of any useful QRM for market risk is an adequate description of volatility dynamics. For example, the adaptive market risk model introduced in the previous chapter relies heavily on the correct treatment of changes in market volatility. In contrast, the ordinary historical simulation approach with equally weighted scenarios had difficulties in adjusting to changing market volatilities, and as a consequence produced a
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