Advances in Portfolio Construction and Implementation

Advances in Portfolio Construction and Implementation

by Alan Scowcroft, Stephen Satchell
Released August 2003
Publisher(s): Butterworth-Heinemann
ISBN: 9780080471846

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Book description

Modern Portfolio Theory explores how risk averse investors construct portfolios in order to optimize market risk against expected returns. The theory quantifies the benefits of diversification.

Modern Portfolio Theory provides a broad context for understanding the interactions of systematic risk and reward. It has profoundly shaped how institutional portfolios are managed, and has motivated the use of passive investment management techniques, and the mathematics of MPT is used extensively in financial risk management.

Advances in Portfolio Construction and Implementation offers practical guidance in addition to the theory, and is therefore ideal for Risk Mangers, Actuaries, Investment Managers, and Consultants worldwide. Issues are covered from a global perspective and all the recent developments of financial risk management are presented. Although not designed as an academic text, it should be useful to graduate students in finance.

*Provides practical guidance on financial risk management
*Covers the latest developments in investment portfolio construction
*Full coverage of the latest cutting edge research on measuring portfolio risk, alternatives to mean variance analysis, expected returns forecasting, the construction of global portfolios and hedge portfolios (funds)

Table of contents

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

Product information

  • Title: Advances in Portfolio Construction and Implementation
  • Author(s): Alan Scowcroft, Stephen Satchell
  • Release date: August 2003
  • Publisher(s): Butterworth-Heinemann
  • ISBN: 9780080471846