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
- Part VIII A framework for choice
- Part IX Numerical implementation
- Part X Analysis of portfolio allocation
- 26 The full allocation procedure: a case study
- 27 Numerical analysis
- 28 Stability analysis
- 29 How to use Bayesian nets: our recommended approach
- Appendix I The links with the Black–Litterman approach
- References
- Index
29 - How to use Bayesian nets: our recommended approach
from Part X - Analysis of portfolio allocation
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
- Part VIII A framework for choice
- Part IX Numerical implementation
- Part X Analysis of portfolio allocation
- 26 The full allocation procedure: a case study
- 27 Numerical analysis
- 28 Stability analysis
- 29 How to use Bayesian nets: our recommended approach
- Appendix I The links with the Black–Litterman approach
- References
- Index
Summary
Some preliminary qualitative observations
What lessons can the prudent portfolio manager draw from the sensitivity analysis presented in the previous chapter?
The strong dependence of the recommended allocations on the most-difficult-to-ascertain set of quantities, the expected returns, may, at first blush, appear somewhat dispiriting. Indeed, after running our sensitivity analysis with behaviourally plausible coefficients of risk aversion, one can justifiably conclude that it would be foolhardy to rely on even the most careful construction of a Bayesian net to ‘read off’ with confidence a single set of allocations from a graph such as the one in Figure 26.11: yes, we may well read a precise set of weights today, but small, unavoidable changes in the input expected returns tomorrow (changes which have nothing directly to do with the Bayesian-net method per se) could give rise to very different allocations. How can we get around the problem of the instability of the optimal allocations to the various asset classes?
To answer this question, we stress again that the sensitivity of the allocations to small changes in expected returns is not an artifact, or a peculiar feature, of the Bayesian-net approach. The analysis presented in the previous chapter (but see also in this respect Chapter 8) shows that it is an unavoidable feature of all allocation methods based on mean-variance optimization.
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- Portfolio Management under StressA Bayesian-Net Approach to Coherent Asset Allocation, pp. 453 - 464Publisher: Cambridge University PressPrint publication year: 2014