Table Of Content

ADVANCES IN PORTFOLIO CONSTRUCTION AND IMPLEMENTATION Butterworth-Heinemann Finance aims and objectives • books based on the work of financial market practitioners,and academics • presenting cutting edge research to the professional/practitionermarket • combining intellectualrigour and practical application • covering the interaction between mathematical theory and financial practice • to improve portfolio performance, risk management and trading book performance • covering quantitativetechniques market Brokers/Traders; Actuaries; Consultants; Asset Managers; Fund Managers; Regulators; Central Bankers; Treasury Officials; Technical Analysts;and Academics for Masters in Finance and MBA market. series titles Return Distributionsin Finance DerivativeInstruments: theory, valuation,analysis Managing DownsideRisk in Financial Markets: theory, practice & implementation Economics for Financial Markets Performance Measurement in Finance: firms, funds and managers Real R&D Options Forecasting Volatilityin the Financial Markets Advanced Trading Rules Advances in Portfolio Construction and Implementation series editor Dr Stephen Satchell Dr Satchell is the Reader in Financial Econometrics at Trinity College, Cambridge; VisitingProfessoratBirkbeckCollege,CityUniversityBusinessSchoolandUniversity of Technology, Sydney. He also works in a consultative capacity to many firms, and edits the journal Derivatives: use, trading and regulations and the Journal of Asset Management. ADVANCES IN PORTFOLIO CONSTRUCTION AND IMPLEMENTATION Edited by Stephen Satchell Alan Scowcroft AMSTERDAM BOSTON HEIDELBERG LONDON NEWYORK OXFORD PARIS SANDIEGO SANFRANCISCO SINGAPORE SYDNEY TOKYO Butterworth-Heinemann An imprint of Elsevier Linacre House, Jordan Hill, Oxford OX2 8DP 200 Wheeler Road, Burlington MA 01803 First published2003 Copyright  2003, Elsevier Ltd. All rights reserved Except Chapter 9  Salomon Smith Barney Inc. All rights reserved Nopartofthispublicationmaybereproducedinanymaterialform(including photocopyingorstoringinanymediumbyelectronicmeansandwhether ornottransientlyorincidentallytosomeotheruseofthispublication)without thewrittenpermissionofthecopyrightholderexceptinaccordancewiththe provisionsoftheCopyright,DesignsandPatentsAct1988orunderthetermsof alicenceissuedbytheCopyrightLicensingAgencyLtd,90TottenhamCourtRoad, London,EnglandW1T4LP.Applicationsforthecopyrightholder’swritten permissiontoreproduceanypartofthispublicationshouldbeaddressed tothepublisher PermissionsmaybesoughtdirectlyfromElsevier’sScienceandTechnologyRights DepartmentinOxford,UK:phone:(+44)(0)1865843830;fax:(+44)(0)1865 853333;e-mail:[email protected] on-lineviatheElsevierSciencehomepage(http://www.elsevier.com),byselecting ‘CustomerSupport’andthen‘ObtainingPermissions’ British Library Cataloguing in Publication Data Advances in portfolio construction and implementation 1. Portfolio management 2. Risk management I. Satchell, Stephen E. II. Scowcroft, A. 332.6 ISBN 0 7506 5448 1 Library of Congress Cataloguing in Publication Data A catalogue record for this book is availablefrom the Library of Congress ISBN 0 7506 5448 1 ForinformationonallButterworth-Heinemannfinancepublications visitourwebsiteatwww.bh.com/finance Typeset by Laserwords Private Limited, Chennai, India Printed and bound in Great Britain Contents List of Contributors xi Introduction xvii 1 A review of portfolio planning: models and systems 1 Gautam Mitra 1.1 Introduction and Overview 2 1.2 Alternative Computational Models 4 1.3 Symmetric and Asymmetric Measures of Risk 14 1.4 Computational Models in Practice 18 1.5 Preparation of Data: Financial Data Marts 22 1.6 Solution Methods 25 1.7 Computational Experience 27 1.8 Discussions and Conclusions 32 1.9 Appendix 1: Piecewise Linear Approximation of the Quadratic Form 32 1.10 Appendix 2: Comparative Computational Views of the Alternative Models 34 References 37 Web References 39 Acknowledgements 39 2 Generalized mean-variance analysis and robust portfolio diversification 40 Stephen M Wright andS E Satchell 2.1 Introduction 41 2.2 Generalized Mean-Variance Analysis 44 vi Contents 2.3 The State Preference Theory Approach to Portfolio Construction 46 2.4 Implementation and Simulation 48 2.5 Conclusions and Suggested Further Work 53 References 54 3 Portfolio construction from mandate to stock weight: a practitioner’s perspective 55 Dr Julian Coutts 3.1 Introduction 56 3.2 Allocating Tracking Error for Multiple Portfolio Funds 58 3.3 Tracking Errors for Arbitrary Portfolios 64 3.4 Active CAPM, or How Far Should a Bet be Taken? 82 3.5 Implementing Ideas in Real Stock Portfolios 89 3.6 Conclusions 92 References 94 4 Enhanced indexation 95 Alan Scowcroftand JamesSefton 4.1 Introduction 96 4.2 Constructing a Consistent View 97 4.3 Enhanced Indexing 103 4.4 An Illustrative Example: Top-down or Bottom-up? 106 4.5 Conclusions 119 4.6 Appendix 1: Derivation of the Theil–Goldberger Mixed Estimator 120 4.7 Appendix 2: Optimization 121 References 123 Notes 124 5 Portfolio management under taxes 125 Dan diBartolomeo 5.1 Introduction 125 5.2 Do Taxes Really Matter to Investors and Managers? 126 5.3 The Core Problems 127 5.4 The State of the Art 128 5.5 The Multi-Period Aspect 130 5.6 Loss Harvesting 132 5.7 After-Tax Benchmarks 132 Contents vii 5.8 Conclusions 133 References 133 6 Using genetic algorithms to construct portfolios 135 Dr T Wilding 6.1 Limitations of Traditional Mean-Variance Portfolio Optimization 135 6.2 Selecting a Method to Limit the Number of Securities in the Final Portfolio 141 6.3 Practical Construction of a Genetic Algorithm-Based Optimizer 145 6.4 Performance of Genetic Algorithm 150 6.5 Conclusions 158 References 159 7 Near-uniformly distributed, stochastically generated portfolios 161 Richard Dawsonand RichardYoung 7.1 Introduction – A Tractable N-Dimensional Experimental Control 162 7.2 Applications 163 7.3 Dynamic Constraints 165 7.4 Results from the Dynamic Constraints Algorithm 173 7.5 Problems and Limitations with Dynamic Constraints Algorithm 174 7.6 Improvements to the Distribution 175 7.7 Results of the Dynamic Constraints with Local Density Control 180 7.8 Conclusions 185 7.9 Further Work 185 7.10 Appendix 1: Review of Holding Distribution in Low Dimensions with Minimal Constraints 186 7.11 Appendix 2: Probability Distribution of Holding Weight in Monte Carlo Portfolios in N Dimensions with Minimal Constraints 189 7.12 Appendix 3: The Effects of Simple Holding Constraints on Expected Distribution of Asset Holding Weights 190 7.13 Appendix 4: Properties of Hyper-Solids 191 References 192 Notes 192 viii Contents 8 Modelling directional hedge funds–mean, variance and correlation with tracker funds 193 Emmanuel Acar 8.1 Introduction 193 8.2 Mean and Variance of Directional Strategies 194 8.3 Correlation with Tracker Fund 196 8.4 Parameters Estimation 198 8.5 Optimal Allocation 199 8.6 An Empirical Application to the Currency Markets 207 8.7 Conclusions 208 8.8 Appendix 1: Mean and Variance of Directional Strategies 209 8.9 Appendix 2: Correlation with Tracker Fund 210 8.10 Appendix 3: Optimal Allocation 212 References 213 Notes 214 Acknowledgements 214 9 Integrating market and credit risk in fixed income portfolios 215 Alla Giland Yuri Polyakov 9.1 Introduction 215 9.2 How to Measure Market and Credit Risk 216 9.3 The Ways of Constructing Loss Distributions 218 9.4 Components of Credit Risk 220 9.5 Portfolio Approach 227 9.6 Conclusions 232 9.7 Appendix 233 References 240 Notes 242 10 Incorporating skewness and kurtosis in portfolio optimization: a multidimensional efficient set 243 Gustavo M de Athayde andRenato G Floˆres, Jr 10.1 Introduction 243 10.2 The Algebra of Multivariate Moments 246 10.3 The Portfolio Frontier: Expected Return, Skewness and Kurtosis 248 10.4 Conclusion 256 References 256 Notes 257 Contents ix 11 Balancing growth and shortfall probability in continuous time active portfolio management 258 Sid Browne 11.1 Introduction 258 11.2 Some Basics 259 11.3 Active Portfolio Management 261 11.4 Trading off Risk and Return in Active Portfolio Management: Fractional Objectives 265 11.5 Risk-Constrained Minimal Time 266 References 268 12 Assessing the merits of rank-based optimization for portfolio construction 269 Soosung Hwang,Stephen E Satchell andStephen M Wright 12.1 Introduction 269 12.2 Optimal Portfolio with Ranks 270 12.3 Empirical Tests 273 12.4 Conclusions 288 References 289 Notes 289 13 The mean-downside risk portfolio frontier: a non-parametric approach 290 Gustavo M de Athayde 13.1 Introduction 290 13.2 The Mean-DSR Portfolio Frontier: The Traditional Approach 293 13.3 The Multivariate Case 297 13.4 A Kernel Approach 299 13.5 The Kernel Approach to the Multivariate Case 303 13.6 The Mean-DSR Portfolio Frontier Using Kernel Estimates 305 13.7 Asset Pricing 306 13.8 Conclusion 308 References 308 14 Some exact results for efficient portfolios with given returns 310 G H Hillier and S E Satchell 14.1 Introduction 310 14.2 Properties of the Risk Estimator 313 14.3 Properties of the Estimated Portfolio Weights 317 14.4 The Riskless Asset Case 321