Scenario Analysis in Risk Management
Theory and Practice in Finance

Authors: Bertrand K. Hassani



Proposes different solutions to address scenario analysis issues in financial institutions

Provides tutorials to help readers implement these solutions

Applies the solutions to stress testing scenarios

This book focuses on identifying and explaining the key determinants of scenario analysis in the context of operational risk, stress testing and systemic risk, as well as management and planning. Each chapter presents alternative solutions to perform reliable scenario analysis. The author also provides technical notes and describes applications and key characteristics for each of the solutions. In addition, the book includes a section to help practitioners interpret the results and adjust them to real-life management activities. Methodologies, including those derived from consensus strategies, extreme value theory, Bayesian networks, Neural networks, Fault Trees, frequentist statistics and data mining are introduced in such a way as to make them understandable to readers without a quantitative background. Particular emphasis is given to the added value of the implementation of these methodologies.

Table of contents

Front Matter

Introduction

Environment

The Information Set: Feeding the Scenarios

The Consensus Approach

Tilting Strategy: Using Probability Distribution Properties

Leveraging Extreme Value Theory

Fault Trees and Variations

Bayesian Networks

Artificial Neural Network to Serve Scenario Analysis Purposes

Forward-Looking Underlying Information: Working with Time Series

Dependencies and Relationships Between Variables

Back Matter