Mean–Variance Optimization for Asset Allocation

Abstract

The mean–variance model is widely acknowledged as the foundation of portfolio allocation because it provides a framework for analyzing the trade-off between risk and return for gaining diversification benefits. Despite the well-known shortcomings of the model, it is often the starting point for making asset allocation decisions. In this article, the authors briefly review mean–variance optimization and approaches for resolving its limitations by demonstrating backtest results on asset allocation. Feedback from asset managers is also included to explain how optimization methods are applied in practice. <b>TOPICS:</b>Statistical methods, portfolio construction, performance measurement <b>Key Findings</b> ▪ Mean–variance optimization provides a framework for analyzing risk–return trade-offs when making asset allocation decisions. ▪ Sensitivity of the mean–variance model can be addressed with robust models, and the model generates candidate asset allocations that are informative in practice. ▪ Feedback from asset managers suggests that mean–variance optimization along with simulation and various robust methods are being applied in practice for asset allocation.