Risk Model Validation - Risk Books
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Book - Risk Model Validation, Second Edition

By Christian Meyer and Peter Quell

Overview

Quantitative risk models have been presented as one of the causes of the financial crisis that started in 2007. In this fully updated second edition, authors Christian Meyer and Peter Quell give a holistic view of risk models: their construction, appropriateness, validation and why they play such an important role in the financial markets.

Publish date: 26 May 2016

Availability: In stock

£145.00
OR

Book description

This new 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.

Readers will be able to:

  • Evaluate the validity of a model;
  • Judge the model’s quality, consistency and regulatory compliance;
  • Improve a framework for validation; and
  • Tailor a model-risk approach for their institution.

Chapters include:

  • Basics of Quantitative Risk Models
  • How Can a Risk Model Fail?
  • The Regulatory Perspective on Risk Model Validation
  • Validation Toolbox 1: Focus on Model Results
  • Validation Toolbox 2: Focus on Model Assumptions
  • Validation Toolbox 3: Focus on Data and Software
  • Implementing a Model Risk Framework

Book details

ISBN
Book - 9781782722632
Publish date
26 May 2016
Format
Paperback
Size
155mm x 235mm

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

Part I: Introduction

1. Basics of Quantitative Risk Models

  • Thinking About Risk
  • Elements of Quantitative Risk Models
  • A 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

Part II: Model Validation

3. Validation Issues

  • What is Validation?
  • When to Introduce Validation?
  • Who Carries Out the Validation?
  • How to Validate Quantitative Risk Models?

4. The Regulatory Perspective on 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
  • Risk Model Validation in the Fundamental Review of the Trading Book
  • Final Comments

5. Validation Toolbox 1: Focus on Model Results

  • Backtesting in Market Risk and Credit Risk
  • Classical Approach: Static Backtesting
  • Dynamic Backtesting: the Reach of Statistical Tests in Backtesting
  • Backtesting Expected Losses

6. Validation Toolbox 2: Focus on Model Assumptions

  • Benchmarking: Constructing Alternative Risk Models
  • Scenario Analysis: What If?
  • Dependence on Parameters: The Good, the Bad and the Ugly

7. Validation Toolbox 3: Focus on Data and Software

  • Software Testing Sensitivity Analysis
  • Statistical Methods for Validation Of Data
  • The Use Test

Part III: Model Risk

8. The Emergence of Model Risk

  • The Concept of Model Risk and its Implications
  • Untangling the Different Levels of Model Risk
  • Regulatory Perspectives on Model Risk
  • The Reach of Model Risk Quantification and related Dangers

9. Implementing a Model Risk Framework

  • The Big Picture: Main Components and their Interaction
  • The Perspective on Models: Processes
  • The Perspective on Models: Results
  • How to establish a Model Validation Committee?
  • A Field Report from Market Risk Validation

10. Model Risk in Market Risk Models: The Short Term Perspective

  • Sources of Model Risk: The Components of Market Risk Models
  • Minimal Requirements: Typical Characteristics of Financial Markets Data
  • The classical Assumptions: Equilibrium and Stationarity
  • Learning: The Market changes - can Models adapt?
  • What are Limits to Adaptation and Learning from Historical Data?

11. Defining a Benchmark Market Risk Model

  • Quick Wins: Which Components are already there?
  • Innovation: How to integrate the Adaptation?
  • Comprehensive Model Definition
  • Model Behaviour in the Lab: Monte Carlo Experiments
  • Model Behaviour in the Wild: Using Historical Market Data

12. Model Risk in Market Risk Models: The Long Term Perspective

  • Market Risk Models in Pillar I: Moving from 1 Day Perspective to 10 Day Perspective
  • Market Risk Models in Pillar II: 1 Year Perspective and Beyond
  • Synopsis: Some Results on Scaling and Long Term Perspectives

13. Model Risk in Portfolio Models for Credit Risk

  • The Components of Credit Risk Models
  • Model Specification - Is there a Choice?
  • Model Parameterization
  • Risk Contributions and Portfolio Effects

14. Model Risk in Portfolio

  • Models for Credit Risk 2
  • Parameter Uncertainty Estimation Uncertainty
  • Backtesting and Alternatives

15. Multiple Models

  • The Benchmark Model revisited
  • The case for Multiple Risk Models
  • A view on Model Averaging
  • A view on Model Mixing
  • Occam’s Razor: When are models simple enough?
  • Epilogue

16. Conclusion – Risk Model Frameworks

  • The Modelling and Implementation Framework
  • The Validation Framework
  • The Model Risk Framework
  • Usage of Risk Models

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