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
- Appendix I The links with the Black–Litterman approach
- References
- Index
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
- Appendix I The links with the Black–Litterman approach
- References
- Index
Summary
In Parts I to IX we have presented the tools required to build a coherent asset allocation in the presence of stress events. In the last part of the book we look in detail at the allocations obtained using this procedure from three different perspectives.
In Chapter 26 we obtain the optimal allocations for a simple but realistic problem; we discuss the properties of the solution; and, what is most important, we try to understand the intuition behind the results.
In Chapter 27 we explore how well the many approximate numerical techniques that we have described in the previous chapters actually work. As we shall see, the answer is that they are surprisingly effective, and that they can therefore greatly reduce the computational burden of the optimization.
In Chapter 28 – one of the most important of the book – we look at the sensitivity of the results to the unavoidable uncertainty in the inputs. We shall discover that the allocations are very sensitive to the precise values of the expected returns. We hasten to stress that the high sensitivity of the results to the expected returns is not brought about by the Bayesian-net methodology – if anything, the latter goes some way towards stabilizing the results. But, as we shall see, there is something rather deep a bout asset allocation in general, and this lack of stability in particular, that these results will bring to light.
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- Portfolio Management under StressA Bayesian-Net Approach to Coherent Asset Allocation, pp. 399 - 402Publisher: Cambridge University PressPrint publication year: 2014