Risk Model Validation - Risk Books
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Risk Model Validation

By Christian Meyer and Peter Quell


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.

Publish date: 30 Mar 2011

Availability: In stock

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Risk Model Validation

Book description

Senior management are expected to make crucial business decisions using complex risk models that, without specialized quantitative financial knowledge, can lead to ill judged choices. The recent controversial discussions concerning the use of risk models during the financial crisis, and the new regulatory framework, have highlighted the need for a consistent approach to answer the question “What are risk models made for?” and maybe even more importantly “What are risk models NOT made for?”.

The report aims to explain:

  • What risk model validation is;
  • What risk models exist;
  • How a risk model can fail;
  • Which aspects of reality are included, and which aspects are excluded from a risk model; and
  • How business decisions can be based on a risk models’ output.

In addressing these issues, this report provides practical advice to the management of financial institutions and a toolbox to raise the key questions when it comes to integrating the results of quantitative models into business decisions.

Book details

Book 9781906348519 / EBook 9781908823274
Publish date
30 Mar 2011

Author biography

Christian Meyer and Peter Quell

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.

Table of contents


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?






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


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



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