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
- 24 Optimizing the expected utility over the weights
- 25 Approximations
- Part X Analysis of portfolio allocation
- Appendix I The links with the Black–Litterman approach
- References
- Index
24 - Optimizing the expected utility over the weights
from Part IX - Numerical implementation
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
- 24 Optimizing the expected utility over the weights
- 25 Approximations
- Part X Analysis of portfolio allocation
- Appendix I The links with the Black–Litterman approach
- References
- Index
Summary
The purpose of this chapter
The purpose of this chapter is to show how to combine the information from the spliced joint distribution of changes in market risk factors (made up of the ‘body’ and of the excited components) with the chosen utility function to obtain a coherent allocation to the various sub-portfolios in the presence of (i.e., taking into due account) stress events. The adjective ‘coherent’ stresses that the allocation has been arrived at by taking into account in a consistent manner the investor's preferences over the outcomes associated with both normal and exceptional market conditions. In our methodology, ‘protection trades’ (such as, say, purchasing out-of-the-money puts or buying CDS protection) are not attached as an ex post afterthought to an optimization previously carried out assuming a stable investing universe. Rather, they are an integral and, again, coherent part of the process.
The emphasis of this chapter is computational. In this respect, it is arguably less ‘exciting’ than the conceptual and methodological parts of the book. However, there is little point in developing interesting ideas unless they can be implemented effectively. From this perspective this apparently mundane chapter is therefore one of the most important of the book.
We also present in this chapter a method to explore the sensitivity of the outputs to one important component of the Bayesian-net construction, i.e., the probability mass associated with the ‘nothing happens’ event.
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- Portfolio Management under StressA Bayesian-Net Approach to Coherent Asset Allocation, pp. 375 - 383Publisher: Cambridge University PressPrint publication year: 2014