Book contents
- Frontmatter
- Dedication
- Contents
- List of figures
- List of tables
- Acknowledgements
- Part I Our approach in its context
- 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
- 20 Splicing the normal and exceptional distributions
- 21 The links with CAPM and private valuations
- 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
21 - The links with CAPM and private valuations
from Part VII - Working with the full distribution
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
- 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
- 20 Splicing the normal and exceptional distributions
- 21 The links with CAPM and private valuations
- 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
Plan of the chapter
This chapter naturally splits into two parts, which address related but different concerns.
In the first part we look at the assignment of the expected returns obtainable from the spliced distribution of the various risk factors. Assigning this input is delicate and affects greatly the optimal allocation, as we discuss in Chapter 28. We provide a ‘model-assisted’ answer to this problem. We say ‘model-assisted’ instead of ‘model-based’, because the expected returns implied by our subjective views will be guided, but not dictated, by an asset-pricing model.
The model we use is the CAPM. We choose to work with it not because we believe that it is a normatively ‘perfect’ model, but because that it can provide a useful sanity check that our return expectations are at least consistent (say, in terms of ranking) and that they reflect a plausible trade-off between return and risk (with risk understood as variance and covariance).
The treatment in the first part of the chapter almost completely disregards information from market prices. What we are trying to achieve is a degree of model-guided internal self-consistency.
Observed market prices come to the fore in the second part of the chapter (Sections 21.6–21.9). Here we assume that the consistency checks in the first part of the chapter have been carried out to our satisfaction, and we try to ascertain whether, given our views, a security is ‘cheap’ or ‘dear’.
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- Information
- Portfolio Management under StressA Bayesian-Net Approach to Coherent Asset Allocation, pp. 316 - 338Publisher: Cambridge University PressPrint publication year: 2014