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Risk Model Validation 3rd edition

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.

£145.00
Availability: In stock
ISBN
978-1-78272-434-6
 

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.

More Information
ISBN 978-1-78272-434-6
Navision code MQUE3
Publication date 12 November 2020
Size 155mm x 235 mm
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Christian Meyer and Peter Quell

Christian Meyer is a quantitative analyst in the portfolio analytics team for market and credit risk in the risk controlling unit of DZ BANK AG in Frankfurt, where he is responsible for the development of portfolio models for credit risk and spread risk in the banking book and incremental risk in the trading book. Before joining DZ BANK, he worked at KPMG, where he dealt with various audit and consulting aspects of market risk, credit risk and economic capital models in the banking industry. Christian holds a diploma and PhD in mathematics, and is a member of the editorial board of the Journal of Risk Model Validation.

 

Peter Quell is head of the portfolio analytics team for market and credit risk in the risk controlling unit of DZ BANK AG in Frankfurt. Before joining DZ BANK, he was manager at d-fine GmbH, where he dealt with various aspects of risk management systems in the banking industry. Peter holds an MSc in mathematical finance from Oxford University and a PhD in mathematics. He is a founding board member of the Model Risk Managers’ International Association (mrmia.org) and a member of the editorial board of the Journal of Risk Model Validation.

PART I: QUANTITATIVE RISK MODELS

1 Basics of quantitative risk models

2 Usage of statistics in quantitative risk models

3 How can a risk model fail?

 

PART II: MODEL RISK AND RISK MODEL VALIDATION

4 The concepts of model risk and validation

5 Model risk frameworks

6 Validation tools

7 Regulation

 

PART III: MODEL RISK IN MARKET RISK MODELS 193

8 Stylised facts and classical approaches

9 Benchmarking with machine learning

10 Extending the risk horizon

PART IV: MODEL RISK IN CREDIT RISK MODELS

 

11 Modelling and simulation

12 Data

13 Model results

14 Impact of machine learning, outlook and conclusions