Publisher Butterworth Heinemann
2003
Contents
Front Cover
Advances in Portfolio Construction and Implementation
Copyright Page
Contents
List of Contributors
Introduction
Chapter 1. A review of portfolio planning: models and systems
1.1 Introduction and Overview
1.2 Alternative Computational Models
1.3 Symmetric and Asymmetric Measures of Risk
1.4 Computational Models in Practice
1.5 Preparation of Data: Financial Data Marts
1.6 Solution Methods
1.7 Computational Experience
1.8 Discussions and Conclusions
1.9 Appendix 1: Piecewise Linear Approximation of the Quadratic Form
1.10 Appendix 2: Comparative Computational Views of the Alternative Models
References
Web References
Acknowledgements
Chapter 2. Generalized mean-variance analysis and robust portfolio diversification
2.1 Introduction
2.2 Generalized Mean-Variance Analysis
2.3 The State Preference Theory Approach to Portfolio Construction
2.4 Implementation and Simulation
2.5 Conclusions and Suggested Further Work
References
Chapter 3. Portfolio construction from mandate to stock weight: a practitioner's perspective
3.1 Introduction
3.2 Allocating Tracking Error for Multiple Portfolio Funds
3.3 Tracking Errors for Arbitrary Portfolios
3.4 Active CAPM, or How Far Should a Bet be Taken?
3.5 Implementing Ideas in Real Stock Portfolios
3.6 Conclusions
References
Chapter 4. Enhanced indexation
4.1 Introduction
4.2 Constructing a Consistent View
4.3 Enhanced Indexing
4.4 An Illustrative Example: Top-down or Bottom-up?
4.5 Conclusions
4.6 Appendix 1: Derivation of the Theil-Goldberger Mixed Estimator
4.7 Appendix 2: Optimization
References
Notes
Chapter 5. Portfolio management under taxes
5.1 Introduction
5.2 Do Taxes Really Matter to Investors and Managers?
5.3 The Core Problems
5.4 The State of the Art
5.5 The Multi-Period Aspect
5.6 Loss Harvesting
5.7 After-Tax Benchmarks
5.8 Conclusions
References
Chapter 6. Using genetic algorithms to construct portfolios
6.1 Limitations of Traditional Mean-Variance Portfolio Optimization
6.2 Selecting a Method to Limit the Number of Securities in the Final Portfolio
6.3 Practical Construction of a Genetic Algorithm-Based Optimizer
6.4 Performance of Genetic Algorithm
6.5 Conclusions
References
Chapter 7. Near-uniformly distributed, stochastically generated portfolios
7.1 Introduction - A Tractable N-Dimensional Experimental Control
7.2 Applications
7.3 Dynamic Constraints
7.4 Results from the Dynamic Constraints Algorithm
7.5 Problems and Limitations with Dynamic Constraints Algorithm
7.6 Improvements to the Distribution
7.7 Results of the Dynamic Constraints with Local Density Control
7.8 Conclusions
7.9 Further Work
7.10 Appendix 1: Review of Holding Distribution in Low Dimensions with Minimal Constraints
7.11 Appendix 2: Probability Distribution of Holding Weight in Monte Carlo Portfolios in N Dimensions with Minimal Constraints
7.12 Appendix 3: The Effects of Simple Holding Constraints on Expected Distribution of Asset Holding Weights
7.13 Appendix 4: Properties of Hyper-Solids
References
Notes
Chapter 8. Modelling directional hedge funds-mean, variance and correlation with tracker funds
8.1 Introduction
8.2 Mean and Variance of Directional Strategies
8.3 Correlation with Tracker Fund
8.4 Parameters Estimation
8.5 Optimal Allocation
8.6 An Empirical Application to the Currency Markets
8.7 Conclusions
8.8 Appendix 1: Mean and Variance of Directional Strategies
8.9 Appendix 2: Correlation with Tracker Fund
8.10 Appendix 3: Optimal Allocation
References
Notes
Acknowledgements
Chapter 9. Integrating market and credit risk in fixed income portfolios
9.1 Introduction
9.2 How to Measure Market and Credit Risk
9.3 The Ways of Constructing Loss Distributions
9.4 Components of Credit Risk
9.5 Portfolio Approach
9.6 Conclusions
9.7 Appendix
References
Notes
Chapter 10. Incorporating skewness and kurtosis in portfolio optimization: a multidimensional efficient set
10.1 Introduction
10.2 The Algebra of Multivariate Moments
10.3 The Portfolio Frontier: Expected Return, Skewness and Kurtosis
10.4 Conclusion
References
Notes
Chapter 11. Balancing growth and shortfall probability in continuous time active portfolio management
11.1 Introduction
11.2 Some Basics
11.3 Active Portfolio Management
11.4 Trading off Risk and Return in Active Portfolio Management: Fractional Objectives
11.5 Risk-Constrained Minimal Time
References
Chapter 12. Assessing the merits of rank-based optimization for portfolio construction
12.1 Introduction
12.2 Optimal Portfolio with Ranks
12.3 Empirical Tests
12.4 Conclusions
References
Notes
Chapter 13. The mean-downside risk portfolio frontier: a non-parametric approach
13.1 Introduction
13.2 The Mean-DSR Portfolio Frontier: The Traditional Approach
13.3 The Multivariate Case
13.4 A Kernel Approach
13.5 The Kernel Approach to the Multivariate Case
13.6 The Mean-DSR Portfolio Frontier Using Kernel Estimates
13.7 Asset Pricing
13.8 Conclusion
References
Chapter 14. Some exact results for efficient portfolios with given returns
14.1 Introduction
14.2 Properties of the Risk Estimator
14.3 Properties of the Estimated Portfolio Weights
14.4 The Riskless Asset Case
14.5 Conclusions
14.6 Appendix: The Unconditional Mean of
References
Notes
Chapter 15. Optimal asset allocation for endowments: A large deviations approach
15.1 Introduction
15.2 The Asset Allocation Model
15.3 An Illustrative Example
15.4 Conclusions
References
Notes
Acknowledgements
Chapter 16. Methods of relative portfolio optimization
16.1 Introduction
16.2 Some Background on Relative Portfolio Optimization
16.3 Model Approaches for Relative Portfolio Optimization
16.4 Discussion of the Models
16.5 Conclusion
References
Notes
Chapter 17. Predicting portfolio returns using the distributions of efficient set portfolios
17.1 Introduction
17.2 Efficient Set Mathematics for Given ﾵ and V
17.3 The Effect of Forecasts
17.4 Model and Process
17.5 Data and Empirical Results
17.6 Conclusions
17.7 Appendix: Effect of Estimation Error in ﾵ
References
Notes
Acknowledgements
Index
Advances in Portfolio Construction and Implementation
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