What is included with this book?
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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