Description: 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. Riccardo Rebonato is Global Head of Rates and FX Analytics at PIMCO, and a visiting lecturer in Mathematical Finance at Oxford University (OCIAM). He has previously held positions as Head of Risk Management and Head of Derivatives Trading at several major international financial institutions. Dr Rebonato has been on the Board of ISDA (2002-2011) and still serves on the Board of GARP (2001 to present). He is the author of several books in finance and an editor for several journals (International Journal of Theoretical and Applied Finance, Journal of Risk, Applied Mathematical Finance, Journal of Risk for Financial Institutions).
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EAN: 9781107048119
UPC: 9781107048119
ISBN: 9781107048119
MPN: N/A
Book Title: Portfolio Management under Stress: A Bayesian-Net
Item Length: 24.9 cm
Number of Pages: 518 Pages
Publication Name: Portfolio Management under Stress: a Bayesian-Net Approach to Coherent Asset Allocation
Language: English
Publisher: Cambridge University Press
Item Height: 244 mm
Subject: Finance
Publication Year: 2014
Type: Textbook
Item Weight: 1070 g
Author: Alexander Denev, Riccardo Rebonato
Item Width: 170 mm
Format: Hardcover