Coherent stress testing : a Bayesian approach to the analysis of financial stress / Riccardo Rebonato.

Format:

Book

Author/Creator:

Rebonato, Riccardo.

Language:

English

Subjects (All):

Risk management.

Probabilities.

Bayesian statistical decision theory.

Physical Description:

1 online resource (241 p.)

Edition:

1st ed.

Place of Publication:

Hoboken, NJ : Wiley, 2010.

Language Note:

English

Summary:

In Coherent Stress Testing: A Bayesian Approach, industry expert Riccardo Rebonato presents a groundbreaking new approach to this important but often undervalued part of the risk management toolkit. Based on the author's extensive work, research and presentations in the area, the book fills a gap in quantitative risk management by introducing a new and very intuitively appealing approach to stress testing based on expert judgement and Bayesian networks. It constitutes a radical departure from the traditional statistical methodologies based on Economic Capital or Extreme-Value-Theory approaches. The book is split into four parts. Part I looks at stress testing and at its role in modern risk management. It discusses the distinctions between risk and uncertainty, the different types of probability that are used in risk management today and for which tasks they are best used. Stress testing is positioned as a bridge between the statistical areas where VaR can be effective and the domain of total Keynesian uncertainty. Part II lays down the quantitative foundations for the concepts described in the rest of the book. Part III takes readers through the application of the tools discussed in part II, and introduces two different systematic approaches to obtaining a coherent stress testing output that can satisfy the needs of industry users and regulators. In part IV the author addresses more practical questions such as embedding the suggestions of the book into a viable governance structure.

Contents:

Coherent Stress Testing

Contents

Acknowledgements

1 Introduction

1.1 Why We Need Stress Testing

1.2 Plan of the Book

1.3 Suggestions for Further Reading

I Data, Models and Reality

2 Risk and Uncertainty - or, Why Stress Testing is Not Enough

2.1 The Limits of Quantitative Risk Analysis

2.2 Risk or Uncertainty?

2.3 Suggested Reading

3 The Role of Models in Risk Management and Stress Testing

3.1 How Did We Get Here?

3.2 Statement of the Two Theses of this Chapter

3.3 Defence of the First Thesis (Centrality of Models)

3.3.1 Models as Indispensable Interpretative Tools

3.3.2 The Plurality-of-Models View

3.4 Defence of the Second Thesis (Coordination)

3.4.1 Traders as Agents

3.4.2 Agency Brings About Coordination

3.4.3 From Coordination to Positive Feedback

3.5 The Role of Stress and Scenario Analysis

3.6 Suggestions for Further Reading

4 What Kind of Probability Do We Need in Risk Management?

4.1 Frequentist versus Subjective Probability

4.2 Tail Co-dependence

4.3 From Structural Models to Co-dependence

4.4 Association or Causation?

4.5 Suggestions for Further Reading

II The Probabilistic Tools and Concepts

Probability with Boolean Variables I: Marginal and Conditional Probabilities

5.1 The Set-up and What We are Trying to Achieve

5.2 (Marginal) Probabilities

5.3 Deterministic Causal Relationship

5.4 Conditional Probabilities

5.5 Time Ordering and Causation

5.6 An Important Consequence: Bayes' Theorem

5.7 Independence

5.8 Two Worked-Out Examples

5.8.1 Dangerous Running

5.8.2 Rare and Even More Dangerous Diseases

5.9 Marginal and Conditional Probabilities: A Very Important Link

5.10 Interpreting and Generalizing the Factors

5.11 Conditional Probability Maps

6 Probability with Boolean Variables II: Joint Probabilities.

6.1 Conditioning on More Than One Event

6.2 Joint Probabilities

6.3 A Remark on Notation

6.4 From the Joint to the Marginal and the Conditional Probabilities

6.5 From the Joint Distribution to Event Correlation

6.6 From the Conditional and Marginal to the Joint Probabilities?

6.7 Putting Independence to Work

6.8 Conditional Independence

6.9 Obtaining Joint Probabilities with Conditional Independence

6.10 At a Glance

6.11 Summary

6.12 Suggestions for Further Reading

7 Creating Probability Bounds

7.1 The Lay of the Land

7.2 Bounds on Joint Probabilities

7.3 How Tight are these Bounds in Practice?

8 Bayesian Nets I: An Introduction

8.1 Bayesian Nets: An Informal Definition

8.2 De.ning the Structure of Bayesian Nets

8.3 More About Conditional Independence

8.4 What Goes in the Conditional Probability Tables?

8.5 Useful Relationships

8.6 A Worked-Out Example

8.7 A Systematic Approach

8.8 What Can We Do with Bayesian Nets?

8.8.1 Unravelling the Causal Structure

8.8.2 Estimating the Joint Probabilities

8.9 Suggestions for Further Reading

9 Bayesian Nets II: Constructing Probability Tables

9.1 Statement of the Problem

9.2 Marginal Probabilities - First Approach

9.2.1 Starting from a Fixed Probability

9.2.2 Starting from a Fixed Magnitude of the Move

9.3 Marginal Probabilities - Second Approach

9.4 Handling Events of Different Probability

9.5 Conditional Probabilities: A Reasonable Starting Point

9.6 Conditional Probabilities: Checks and Constraints

9.6.1 Necessary Conditions

9.6.2 Triplet Conditions

9.6.3 Independence

9.6.4 Deterministic Causation

9.6.5 Incompatibility of Events

9.7 Internal Compatibility of Conditional Probabilities: The Need for a Systematic Approach

III Applications.

10 Obtaining a Coherent Solution I: Linear Programming

10.1 Plan of the Work Ahead

10.2 Coherent Solution with Conditional Probabilities Only

10.3 The Methodology in Practice: First Pass

10.4 The CPU Cost of the Approach

10.5 Illustration of the Linear Programming Technique

10.6 What Can We Do with this Information?

10.6.1 Extracting Information with Conditional Probabilities Only

10.6.2 Extracting Information with Conditional and Marginal Probabilities

11 Obtaining a Coherent Solution II: Bayesian Nets

11.1 Solution with Marginal and n-conditioned Probabilities

11.1.1 Generalizing the Results

11.2 An 'Automatic' Prescription to Build Joint Probabilities

11.3 What Can We Do with this Information?

11.3.1 Risk-Adjusting Returns

IV Making It Work In Practice

12 Overcoming Our Cognitive Biases

12.1 Cognitive Shortcomings and Bounded Rationality

12.1.1 How Pervasive are Cognitive Shortcomings?

12.1.2 The Social Context

12.1.3 Adaptiveness

12.2 Representativeness

12.3 Quantification of the Representativeness Bias

12.4 Causal/Diagnostic and Positive/Negative Biases

12.5 Conclusions

12.6 Suggestions for Further Reading

13 Selecting and Combining Stress Scenarios

13.1 Bottom Up or Top Down?

13.2 Relative Strengths and Weaknesses of the Two Approaches

13.3 Possible Approaches to a Top-Down Analysis

13.4 Sanity Checks

13.5 How to Combine Stresses - Handling the Dimensionality Curse

13.6 Combining the Macro and Bottom-Up Approaches

14 Governance

14.1 The Institutional Aspects of Stress Testing

14.1.1 Transparency and Ease of Use

14.1.2 Challenge by Non-specialists

14.1.3 Checks for Completeness

14.1.4 Interactions among Different Specialists

14.1.5 Auditability of the Process and of the Results

14.2 Lines of Criticism.

14.2.1 The Role of Subjective Inputs

14.2.2 The Complexity of the Stress-testing Process

Appendix A Simple Introduction to Linear Programming

A.1 Plan of the Appendix

A.2 Linear Programming - A Refresher

A.3 The Simplex Method

References

Index.

Notes:

Bibliographic Level Mode of Issuance: Monograph

Description based on metadata supplied by the publisher and other sources.

ISBN:

9786612683787

9780470971482

0470971487

9781118374719

1118374711

9781282683785

1282683780

9780470667361

0470667362

OCLC:

645098899