Riccardo Rebonato
Coherent Stress Testing
Riccardo Rebonato
Coherent Stress Testing
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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…mehr
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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.
Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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.
Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Produktdetails
- Produktdetails
- Wiley Finance Series
- Verlag: Wiley & Sons
- 1. Auflage
- Seitenzahl: 240
- Erscheinungstermin: 13. Juli 2010
- Englisch
- Abmessung: 250mm x 175mm x 18mm
- Gewicht: 582g
- ISBN-13: 9780470666012
- ISBN-10: 0470666013
- Artikelnr.: 29747959
- Wiley Finance Series
- Verlag: Wiley & Sons
- 1. Auflage
- Seitenzahl: 240
- Erscheinungstermin: 13. Juli 2010
- Englisch
- Abmessung: 250mm x 175mm x 18mm
- Gewicht: 582g
- ISBN-13: 9780470666012
- ISBN-10: 0470666013
- Artikelnr.: 29747959
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.
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. 5 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 xk i. 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 Defining 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.
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. 5 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 xk i. 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 Defining 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.
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. 5 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 xk i. 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 Defining 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.
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. 5 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 xk i. 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 Defining 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.