Risk Model Validation (3rd edition)
First published:
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Modelling and simulation
Modelling and simulation
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
Granting credit – that is, lending money – is the traditional core business of banks. Obligors pay interest on their loans, and repay their loans at maturity. Or they do not, in which case the lending bank will try to get back as much of the money as possible, but in most cases suffer some loss. This potential for loss is at the heart of what is called “credit risk”, and it is usually one of the main concerns of a bank’s risk management. Moreover, credit risk has gained enormous importance in the regulatory context (see Chapter 7).
In this chapter, we will describe credit risk in more detail along the framework introduced earlier – ie, by thinking about the quantity of interest, the set of potential future scenarios and the risk measure. A toy, albeit quite a serious, model will be set up, and a test portfolio will be defined. This will be the starting point for a discussion on model risk in, and validation of, credit risk models, at the end of this chapter and in the following two chapters. For a detailed introduction to credit risk modelling, see Bluhm, Overbeck and Wagner (2010) and Benvegnù, Bluhm and Müller (2008).
DEFINITION OF CREDIT RISK
In Chapter 1, a framework
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