9780470666012

Coherent Stress Testing A Bayesian Approach to the Analysis of Financial Stress

by
  • ISBN13:

    9780470666012

  • ISBN10:

    0470666013

  • Format: Hardcover
  • Copyright: 7/13/2010
  • Publisher: Wiley
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Summary

Why a book about stress testing? And why a book about stress testing now? Stress testing has been part of the risk manager's toolkit for decades. What justifies the renewed interest from practitioners and regulators for a risk management tool that has always been the poor relation in the family of analytical techniques to control risk? And why has stress testing so far been regarded as a second-class citizen? Understanding the reason for the renewed interest is simple: the financial crisis of 2007-2009 has shown with painful clarity the limitations of the purely statistical techniques (such as VaR and Economic Capital) that were supposed to provide the cornerstones of the financial edifice. As once in a lifetime events kept on occurring with disconcerting regularity in the twenty-or-so months starting with July 2007, it became clear that something new was required.Stress Testing: A Coherent Approach presents groundbreaking new approaches to stress testing. Acknowledged industry expert Riccardo Rebonato moves beyond economic capital and VaR approaches to stress testing to offer a new approach to this risk management discipline. The book sets out by looking at the existing models and how they are used (and abused) by the risk manager. It then moves on to look at the different probabilistic approaches that can be taken in the measurement of risk, at well known and less well known quantitative approaches for stress testing, linear programming, before proposing a new approach to stress testing. Finally, the book sets all of the above in the context of financial institution and a new era of regulation and governance.

Author Biography

DR. RICCARDO REBONATO (London, UK) is Head of Front Office Risk Management and Head of the Clients Analytics team at BGM RBS. He is visiting lecturer at Oxford University (Mathematical Finance) and adjunct professor at Imperial College (Tanaka Business School). He sits on the Board of Directors of ISDA and on the Board of Trustees for GARP. He is an editor for the International Journal of Theoretical and Applied Finance, Applied Mathematical Finance, Journal of Risk, and the Journal of Risk Management in Financial Institutions. He holds doctorates in Nuclear Engineering and in Science of Material/Solid State Phsyics. He was a research fellow in Physics at Corpus Christi College, Oxford, UK.

Table of Contents

Acknowledgements
Introduction
Why We Need Stress Testing
Plan of the Book
Suggestions for Further Reading
Data, Models and Reality
Risk and Uncertainty - or, Why Stress Testing is Not Enough
The Limits of Quantitative Risk Analysis
Risk or Uncertainty?
Suggested Reading
The Role of Models in Risk Management and Stress Testing
How Did We Get Here?
Statement of the Two Theses of this Chapter
Defence of the First Thesis (Centrality of Models)
Models as Indispensable Interpretative Tools
The Plurality-of-Models View
Defence of the Second Thesis (Coordination)
Traders as Agents
Agency Brings About Coordination
From Coordination to Positive Feedback
The Role of Stress and Scenario Analysis
Suggestions for Further Reading
What Kind of Probability Do We Need in Risk Management?
Frequentist versus Subjective Probability
Tail Co-dependence
From Structural Models to Co-dependence
Association or Causation?
Suggestions for Further Reading
The Probabilistic Tools and Concepts
Probability with Boolean Variables I: Marginal and Conditional Probabilities
The Set-up and What We are Trying to Achieve
(Marginal) Probabilities
Deterministic Causal Relationship
Conditional Probabilities
Time Ordering and Causation
An Important Consequence: Bayes' Theorem
Independence
Two Worked-Out Examples
Dangerous Running
Rare and Even More Dangerous Diseases
Marginal and Conditional Probabilities: A Very Important Link
Interpreting and Generalizing the Factors xk i
Conditional Probability Maps
Probability with Boolean Variables II: Joint Probabilities
Conditioning on More Than One Event
Joint Probabilities
A Remark on Notation
From the Joint to the Marginal and the Conditional Probabilities
From the Joint Distribution to Event Correlation
From the Conditional and Marginal to the Joint Probabilities?
Putting Independence to Work
Conditional Independence
Obtaining Joint Probabilities with Conditional Independence
At a Glance
Summary
Suggestions for Further Reading
Creating Probability Bounds
The Lay of the Land
Bounds on Joint Probabilities
How Tight are these Bounds in Practice?
Bayesian Nets I: An Introduction
Bayesian Nets: An Informal Definition
Defining the Structure of Bayesian Nets
More About Conditional Independence
What Goes in the Conditional Probability Tables?
Useful Relationships
A Worked-Out Example
A Systematic Approach
What Can We Do with Bayesian Nets?
Unravelling the Causal Structure
Estimating the Joint Probabilities
Suggestions for Further Reading
Bayesian Nets II: Constructing Probability Tables
Statement of the Problem
Marginal Probabilities - First Approach
Starting from a Fixed Probability
Starting from a Fixed Magnitude of the Move
Marginal Probabilities - Second Approach
Handling Events of Different Probability
Conditional Probabilities: A Reasonable Starting Point
Conditional Probabilities: Checks and Constraints
Necessary Conditions
Triplet Conditions
Independence
Deterministic Causation
Incompatibility of Events
Internal Compatibility of Conditional Probabilities: The Need for a Systematic Approach
Applications
Obtaining a Coherent Solution I: Linear Programming
Plan of the Work Ahead
Coherent Solution with Conditional Probabilities Only
The Methodology in Practice: First Pass
The CPU Cost of the Approach
Illustration of the Linear Programming Technique
What Can We Do with this Information?
Extracting Information with Conditional Probabilities Only
Extracting Information with Conditional and Marginal Probabilities
Obtaining a Coherent Solution II: Bayesian Nets
Solution with Marginal and n-conditioned Probabilities
Generalizing the Results
An 'Automatic' Prescription to Build Joint Probabilities
What Can We Do with this Information?
Risk-Adjusting Returns
Making It Work In Practice
Overcoming Our Cognitive Biases
Cognitive Shortcomings and Bounded Rationality
How Pervasive are Cognitive Shortcomings?
The Social Context
Adaptiveness
Representativeness
Quantification of the Representativeness Bias
Causal/Diagnostic and Positive/Negative Biases
Conclusions
Suggestions for Further Reading
Selecting and Combining Stress Scenarios
Bottom Up or Top Down?
Relative Strengths and Weaknesses of the Two Approaches
Possible Approaches to a Top-Down Analysis
Sanity Checks
How to Combine Stresses - Handling the Dimensionality Curse
Combining the Macro and Bottom-Up Approaches
Governance
The Institutional Aspects of Stress Testing
Transparency and Ease of Use
Challenge by Non-specialists
Checks for Completeness
Interactions among Different Specialists
Auditability of the Process and of the Results
Lines of Criticism
The Role of Subjective Inputs
The Complexity of the Stress-testing Process
Simple Introduction to Linear Programming
Plan of the Appendix
Linear Programming - A Refresher
The Simplex Method
References
Index
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