Portfolio Construction and Risk Budgeting
First published:
ISBN: 9781782721000
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Application in Mean–Variance Investing
Introduction
A Primer on Portfolio Theory
Application in Mean–Variance Investing
Diversification
Frictional Costs of Diversification
Risk Parity
Incorporating Deviations from Normality: Lower Partial Moments
Portfolio Resampling and Estimation Error
Robust Portfolio Optimisation and Estimation Error
Bayesian Analysis and Portfolio Choice
Testing Portfolio Construction Methodologies Out-of-Sample
Portfolio Construction with Transaction Costs
Portfolio Optimisation with Options: From the Static Replication of CPPI Strategies to a More General Framework
Scenario Optimisation
Core–Satellite Investing: Budgeting Active Manager Risk
Benchmark-Relative Optimisation
Removing Long-Only Constraints: 120/20 Investing
Performance-Based Fees, Incentives and Dynamic Tracking Error Choice
Long-Term Portfolio Choice
Risk Management for Asset-Management Companies
Valuation of Asset Management Firms
Tail Risk Hedging
2.1 CLUSTERING TECHNIQUES AND THE INVESTMENT UNIVERSE
2.1.1 The correlation problem
Few investors are aware that the definition of the investment universe itself has a considerable impact on the outcome of portfolio construction. If, for example, the investment universe is constrained to government bonds and emerging market bonds, it is obvious that all combinations are efficient and investors are likely to place a considerable allocation in emerging market bonds. However, as soon as high-yield bonds and emerging market equities are introduced, this situation might change due to the correlation between these assets.
To increase the transparency of the asset allocation process and to avoid the accumulation of estimation errors, portfolio optimisation should be limited to groups of assets that have high intragroup and low intergroup correlations.11The selection of asset classes suitable for optimisation is both quantitative and judgemental. The benefit of this correlation-guided asset class definition is a weakened sensitivity of the optimal portfolio solution with respect to mean return estimates (which carry the highest estimation error, as can be seen from Appendix A of Chapter
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