Portfolio Management under Stress
A Bayesian-Net Approach to Coherent Asset Allocation

A rigorous presentation of a novel methodology for asset allocation in financial portfolios under conditions of market distress.

Riccardo Rebonato (Author), Alexander Denev (Author)

9781107048119, Cambridge University Press

Hardback, published 9 January 2014

518 pages, 119 b/w illus.
24.4 x 17 x 2.9 cm, 1.07 kg

'Rebonato and Denev have ploughed for all of us the vast field of applications of Bayesian nets to quantitative risk and portfolio management, leaving absolutely no stone unturned.' Attilio Meucci, Chief Risk Officer at Kohlberg Kravis Roberts (KKR)

Portfolio Management under Stress offers a novel way to apply the well-established Bayesian-net methodology to the important problem of asset allocation under conditions of market distress or, more generally, when an investor believes that a particular scenario (such as the break-up of the Euro) may occur. Employing a coherent and thorough approach, it provides practical guidance on how best to choose an optimal and stable asset allocation in the presence of user specified scenarios or 'stress conditions'. The authors place causal explanations, rather than association-based measures such as correlations, at the core of their argument, and insights from the theory of choice under ambiguity aversion are invoked to obtain stable allocations results. Step-by-step design guidelines are included to allow readers to grasp the full implementation of the approach, and case studies provide clarification. This insightful book is a key resource for practitioners and research academics in the post-financial crisis world.

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: 4. Predictability and causality
5. Econophysics
6. Extreme value theory
Part III. Diversification and Subjective Views
7. Diversification in modern portfolio theory
8. Stability: a first look
9. Diversification and stability in the Black–Litterman model
10. Specifying scenarios: the Meucci approach
Part IV. How We Deal with Exceptional Events: 11. Bayesian nets
12. Building scenarios for causal Bayesian nets
Part V. Building Bayesian Nets in Practice: 13. Applied tools
14. More advanced topics: elicitation
15. Additional more advanced topics
16. A real-life example: building a realistic Bayesian net
Part VI. Dealing with Normal-Times Returns: 17. Identification of the body of the distribution
18. Constructing the marginals
19. Choosing and fitting the copula
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: 22. Applying expected utility
23. Utility theory: problems and remedies
Part IX. Numerical Implementation: 24. Optimizing the expected utility over the weights
25. Approximations
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
30. Appendix I. The links with the Black–Litterman approach
31. Appendix II. Marginals, copulae and the symmetry of return distributions
Index.

Subject Areas: Mathematics [PB], Finance [KFF], Economics [KC]