MARC Record from Library of Congress

LEADER: 03964cam a2200349 i 4500
001 2013037705
003 DLC
005 20140918080314.0
008 130923s2013 enka b 001 0 eng
010 $a 2013037705
020 $a9781107048119 (hardback)
040 $aDLC$beng$cDLC$erda$dDLC
042 $apcc
050 00 $aHG4529.5$b.R43 2013
082 00 $a332.601/519542$223
084 $aBUS027000$2bisacsh
100 1 $aRebonato, Riccardo.
245 10 $aPortfolio management under stress :$ba Bayesian-net approach to coherent asset allocation /$cRiccardo Rebonato and Alexander Denev.
264 1 $aCambridge :$bCambridge University Press,$c2013.
300 $axxvi, 491 pages :$billustrations ;$c25 cm
336 $atext$2rdacontent
337 $aunmediated$2rdamedia
338 $avolume$2rdacarrier
504 $aIncludes bibliographical references (pages 471-484) and index.
520 $a"Portfolio Management Under Stress offers a novel way to apply the well-established Bayesian-net methodology to the important problem of asset allocation under conditions of market distress or, more generally, when an investor believes that a particular scenario (such as the break-up of the Euro) may occur. Employing a coherent and thorough approach, it provides practical guidance on how best to choose an optimal and stable asset allocation in the presence of user-specified scenarios or 'stress conditions'. The authors place causal explanations, rather than association-based measures such as correlations, at the core of their argument, and insights from the theory of choice under ambiguity aversion are invoked to obtain stable allocations results. Step-by-step design guidelines are included to allow readers to grasp the full implementation of the approach, and case studies provide clarification. This insightful book is a key resource for practitioners and research academics in the post-financial crisis world"--$cProvided by publisher.
504 $aIncludes bibliographical references and index.
505 8 $aMachine generated contents note: 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.
650 0 $aPortfolio management$xMathematical models.
650 0 $aInvestments$xMathematical models.
650 0 $aFinancial risk$xMathematical models.
650 7 $aBUSINESS & ECONOMICS / Finance.$2bisacsh
700 1 $aDenev, Alexander.