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

ISBN-13: 9781107048119 / Angielski / Twarda / 2014 / 518 str.

Riccardo Rebonato;Alexander Denev

Opis

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.

Recenzje

'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)

Spis treści

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.