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 guides 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. Readers will be able to: Evaluate the validity of a model; Judge the model’s quality, consistency and regulatory compliance; Establish or improve a framework for validation; See how machine learning can support model development and validation; and Tailor a model-risk approach for their institution. Risk Model Validation (3rd edition) provides financial institutions with a toolbox to raise the key questions when it comes to integrating the results of quantitative risk models into business decisions.
