Benchmark-Relative Optimisation
Benchmark-Relative Optimisation
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
15.1 TRACKING ERROR: SELECTED ISSUES
Tracking error measures the dispersion (volatility) of active returns (portfolio return minus benchmark return) around the mean active return. It is designed as a measure of relative investment risk and was introduced into the academic arena in the early 1980s (see Sharpe 1981). Since then it has become the single most important risk measure in communications between asset manager, client and consultant.
Benchmark-relative investment management has often been rationalised from either a risk perspective (a benchmark anchors the portfolio in risk–return space and thus gives sponsors confidence in what their money is invested in and what risk this investment carries) or a return perspective (claiming that it is easier to forecast relative returns than total returns). However, the return argument looks spurious; to say that forecasting relative distances is possible whereas forecasting absolute distances is not ignores the fact that the two are closely related: a total distance is the same as the benchmark distance times a relative distance. A more plausible argument that is made is that the estimation error is smaller for relative than for
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