Risk Model Validation (3rd edition)
First published:
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Model risk frameworks
Model risk frameworks
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
In Chapter 4, model risk was defined in terms of the potential of a specific QRM implementation not being useful. It can lead to financial loss, poor business and strategic decision-making, or damage to a bank’s reputation (see OCC, 2011). In Chapter 1, risk in general was characterised as a situation with an uncertain future outcome that is of importance to us. Based on this characterisation, model risk would indeed count as a risk.
The immediate questions would then be: “How should model risk be dealt with?” and “Is there a QRM to cope with model risk?” Leaving aside some self-referential aspects for the moment, identification of the basic elements of such a QRM would present a big challenge:
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a quantity of interest the future value of which is uncertain;
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a set of potential future scenarios that describe possible values of that quantity of interest; and
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a statistic or a risk measure to sum up the essential information obtained from the analysis of the potential future scenarios.
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Of course, one could claim that the quantity of interest would be the economic loss due to a misused QRM and that the potential future scenarios would be defined by a
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