Risk Model Validation (3rd edition)
First published:
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Stylised facts and classical approaches
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
Part II of this book provides a general approach to the treatment of model risk. To implement this approach in financial institutions, this chapter will start by analysing QRMs in the context of market risk. Market risk is one of the main risk types of most financial institutions that are involved in trading activities. The dependence on QRMs for market risk has been strengthened increasingly by various initiatives from financial market regulation, such as the Basel framework (banking, see Chapter 7) or Solvency (insurance). This has led financial institutions to embed market risk models in their steering processes as well as capital planning activities.
This chapter will start by briefly recalling the basic components of QRMs for market risk from earlier chapters. Since market risk models need to operate in an environment informed by financial market data, it is necessary to collect the essential characteristics of financial market time series. The descriptions of these “stylised facts” are mostly empirical in nature; thus, this chapter does not provide explanations for the observed behaviour in terms of other theories or models.
It will become clear that one of the most
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