Risk Model Validation

ABOUT THIS BOOK
Worldwide, 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 recognizing the assumptions that underpin them present considerable challenges for all but those with a background in specialized quantitative financial modeling.

The use of these quantitative risk models was blamed as being one of the major causes of the financial crisis that began in 2007. This report shows how risk models are constructed and why they play such an important role in financial markets. It provides a holistic approach to Risk Model Validation that will enable you, when faced with a specific risk model, to work out a step-by-step guide to asking the right questions in order to judge the validity of the model.

Mathematical modelling, implementation, data gathering, processes, reporting and the way senior management “digests” all this information will all be covered.

An essential part of a decision-maker’s armoury, Risk Model Validation provides an intensive guide to asking the key questions when integrating the outputs of quantitative modeling into everyday business decisions.


TABLE OF CONTENTS
Introduction

1 Basics of Quantitative Risk Models

Thinking About Risk

Elements of Quantitative Risk Models

An Historical Example

Usage of Statistics in Quantitative Risk Models

Setup of Quantitative Risk Models

2 How Can a Risk Model Fail?

Design

Implementation

Data

Processes

Use

3 Validation Issues

What is Validation?

When to Introduce Validation

Who Carries Out the Validation?

How to Validate Quantitative Risk Models

4 The Basel Accords and Risk Model Validation

The Pillars of the Basel Framework

Risk Models and their Validation Under Pillar 1

Risk Models and their Validation Under Pillar 2

Stress Testing

Guidance on Validation in Regulatory Documents

Final Comments

5 Tools for Validation of Model Results

Statistical Methods

Benchmarking

Scenario Analysis

6 Other Validation Tools

Software Testing

Sensitivity Analysis

Statistical Methods for Validation Of Data

The Use Test

7 Conclusion – Risk Model Frameworks

The Modelling and Implementation Framework

The Validation Framework

Usage of Risk Models

References

Index


ABOUT THE AUTHORS
Christian Meyer is working as Quantitative Analyst in the Portfolio Modeling 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 in the banking book and incremental risk in the trading book. Prior to joining DZ BANK AG he was working for KPMG where he dealt with various aspects (audit and consulting) of market risk, credit risk, and economic capital models in the banking industry. He holds a diploma and PhD in Mathematics.

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. Prior to joining DZ BANK AG he was Manager at d-fine GmbH where he dealt with various aspects of risk management systems in the banking industry. He holds a MSc. in Mathematical Finance from Oxford University and a PhD in Mathematics.