Book contents
- Frontmatter
- Dedication
- Contents
- List of figures
- List of tables
- Acknowledgements
- Part I Our approach in its context
- 1 How this book came about
- 2 Correlation and causation
- 3 Definitions and notation
- Part II Dealing with extreme events
- Part III Diversification and subjective views
- Part IV How we deal with exceptional events
- Part V Building Bayesian nets in practice
- Part VI Dealing with normal-times returns
- Part VII Working with the full distribution
- Part VIII A framework for choice
- Part IX Numerical implementation
- Part X Analysis of portfolio allocation
- Appendix I The links with the Black–Litterman approach
- References
- Index
2 - Correlation and causation
from Part I - Our approach in its context
Published online by Cambridge University Press: 18 December 2013
- Frontmatter
- Dedication
- Contents
- List of figures
- List of tables
- Acknowledgements
- Part I Our approach in its context
- 1 How this book came about
- 2 Correlation and causation
- 3 Definitions and notation
- Part II Dealing with extreme events
- Part III Diversification and subjective views
- Part IV How we deal with exceptional events
- Part V Building Bayesian nets in practice
- Part VI Dealing with normal-times returns
- Part VII Working with the full distribution
- Part VIII A framework for choice
- Part IX Numerical implementation
- Part X Analysis of portfolio allocation
- Appendix I The links with the Black–Litterman approach
- References
- Index
Summary
We never step in the same river twice. Heraclitus
Statistical versus causal explanations
We stated in the previous chapter that we look at causation as a primary concept and at correlation as a derived one. It is useful to explain in some detail what we mean. Doing so will also help the reader understand why we regard Bayesian nets as more than a tool for factorizing efficiently complex joint probabilities. We think that such a factorization view of Bayesian nets, powerful as it is, is too reductive, and misses their real potential. The ability afforded by Bayesian nets to ‘represent’ (conditional) independence in a transparent and intuitive way is only one of their strengths. The real power of Bayesian nets stems from their ability to describe causal links among variables in a parsimonious and flexible manner. See Pearl (1986, 2009) for a thorough discussion of these points. To use his terminology, casting our treatment in terms of causation will make our judgements about (conditional) (in)dependence ‘robust’; will make them well suited to represent and respond to changes in the external environment; will allow us to work with conceptual tools which are more ‘stable’ than probabilities; will permit extrapolation to situations or combination of events that have not occurred in history.
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- Chapter
- Information
- Portfolio Management under StressA Bayesian-Net Approach to Coherent Asset Allocation, pp. 13 - 22Publisher: Cambridge University PressPrint publication year: 2014