Risk Model Validation (3rd edition)
Discipline: Quantitative Analysis
In this fully updated third edition of Risk Model Validation, authors Christian Meyer and Peter Quell return to give readers a panoptical view of risk models: their construction, appropriateness, validation and why they play such an important role in the financial markets.
Across the globe, senior executives and managers in financial and non-financial firms are expected to make crucial business decisions based on the results of complex risk models. Yet interpreting the findings, understanding the limitations of the models and recognising the assumptions that underpin them can present considerable challenges for all except those with specialised quantitative financial-modelling backgrounds.
On the technological side, machine learning is challenging model validation, and on the regulatory side, there is an increasing interest in model-risk quantification. Risk Model Validation (3rd edition) provides a comprehensive framework with practical examples that guide the reader towards the implementation of a tailor-made validation framework.
The authors lead the reader through the process of risk modelling, demonstrating how to interpret their findings, how to understand the limitations of risk models, and how to identify and challenge the assumptions that reinforce them.
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
Stylised facts and classical approaches
Benchmarking with machine learning
Extending the risk horizon
Modelling and simulation
Impact of machine learning, outlook and conclusions