Risk Model Validation (3rd edition)
First published:
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Data
Data
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
So far, the model has been designed and implemented, and is running smoothly and producing results. However, remember GIGO: the results can only be useful if the model risk behind the input data, ie, model parameters and position-level data, is being addressed properly. In the model we have been using, three sets of data will have to be considered in total:
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the PDs assigned to the positions;
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the EADs and LGD levels assigned to the positions; and
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the correlation parameter ρ.
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In general, the very generation of these data is of a quantitative and statistical nature. It is therefore quite natural to think about the implied model risk, and to formulate criteria for validation, also using quantitative and statistical methods. However, this is not the full story. Credit portfolio models will always suffer from the problem of possible lack of representativeness. The exposures in the current portfolio have not (yet) defaulted (at least most of them) by definition. On the other hand, to estimate parameters statistically, one needs either data from actual defaults or some mechanism (in other words, another model) connecting different
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