Asymmetric Dependence in Finance: Diversification, Correlation and Portfolio Management in Market Downturns

Asymmetric Dependence in Finance: Diversification, Correlation and Portfolio Management in Market Downturns


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Avoid downturn vulnerability by managing correlation dependency

Asymmetric Dependence in Finance examines the risks and benefits of asset correlation, and provides effective strategies for more profitable portfolio management. Beginning with a thorough explanation of the extent and nature of asymmetric dependence in the financial markets, this book delves into the practical measures fund managers and investors can implement to boost fund performance. From managing asymmetric dependence using Copulas, to mitigating asymmetric dependence risk in real estate, credit and CTA markets, the discussion presents a coherent survey of the state-of-the-art tools available for measuring and managing this difficult but critical issue.

Many funds suffered significant losses during recent downturns, despite having a seemingly well-diversified portfolio. Empirical evidence shows that the relation between assets is much richer than previously thought, and correlation between returns is dependent on the state of the market; this book explains this asymmetric dependence and provides authoritative guidance on mitigating the risks.

  • Examine an options-based approach to limiting your portfolio's downside risk
  • Manage asymmetric dependence in larger portfolios and alternate asset classes
  • Get up to speed on alternative portfolio performance management methods
  • Improve fund performance by applying appropriate models and quantitative techniques

Correlations between assets increase markedly during market downturns, leading to diversification failure at the very moment it is needed most. The 2008 Global Financial Crisis and the 2006 hedge-fund crisis provide vivid examples, and many investors still bear the scars of heavy losses from their well-managed, well-diversified portfolios. Asymmetric Dependence in Finance shows you what went wrong, and how it can be corrected and managed before the next big threat using the latest methods and models from leading research in quantitative finance.

Product Details

ISBN-13: 9781119289012
Publisher: Wiley
Publication date: 06/13/2018
Series: Wiley Finance Series
Pages: 312
Product dimensions: 6.90(w) x 9.70(h) x 1.10(d)

About the Author

JAMIE ALCOCK, PHD, is Associate Professor of Finance at the University of Sydney Business School. He has previously held appointments at the University of Cambridge, Downing College Cambridge, and the University of Queensland. Dr. Alcock's research interests include asset pricing, corporate finance, and real estate finance. The quality of his research has been recognized through multiple international research prizes, including most recently the EPRA Best Paper prize at the 2016 European Real Estate Society conference.

STEPHEN SATCHELL, PHD, PHD, is a Life Fellow at Trinity College Cambridge, a Professor of Finance at the University of Sydney and was an Honorary Visiting Professor at Birkbeck College, University of London. He is the Emeritus Reader in Financial Econometrics at the University of Cambridge and is an Honorary Member of the Institute of Actuaries. He specializes in finance and econometrics, on which he has written at least 200 papers.

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Table of Contents

About the Editors ix

Introduction xi

CHAPTER 1 Disappointment Aversion, Asset Pricing and Measuring Asymmetric Dependence 1
Jamie Alcock and Anthony Hatherley

CHAPTER 2 The Size of the CTA Market and the Role of Asymmetric Dependence 17
Stephen Satchell and Oliver Williams

CHAPTER 3 The Price of Asymmetric Dependence 47
Jamie Alcock and Anthony Hatherley

CHAPTER 4 Misspecification in an Asymmetrically Dependent World: Implications for Volatility Forecasting 75
Salman Ahmed, Nandini Srivastava and Stephen Satchell

CHAPTER 5 Hedging Asymmetric Dependence 110
Anthony Hatherley

CHAPTER 6 Orthant Probability-Based Correlation 133
Mark Lundin and Stephen Satchell

CHAPTER 7 Risk Measures Based on Multivariate Skew Normal and Skew t -Mixture Models 152
Sharon X. Lee and Geoffrey J. McLachlan

CHAPTER 8 Estimating Asymmetric Dynamic Distributions in High Dimensions 169
Stanislav Anatolyev, Renat Khabibullin and Artem Prokhorov

CHAPTER 9 Asymmetric Dependence, Persistence and Firm-Level Stock Return Predictability 198
Jamie Alcock and Petra Andrlikova

CHAPTER 10 The Most Entropic Canonical Copula with an Application to ‘Style’ Investment 221
Ba Chu and Stephen Satchell

CHAPTER 11 Canonical Vine Copulas in the Context of Modern Portfolio Management: Are They Worth It? 263
Rand Kwong Yew Low, Jamie Alcock, Robert Faff and Timothy Brailsford

Index 291

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