Strategic Asset Allocation with Mean-Variance Optimisation

Given the importance of strategic asset allocation in explaining the ex post performance of any type of investment portfolio, this chapter provides an in-depth analysis of asset allocation methods, illustrating the different theoretical and operational solutions available to institutional investors. This chapter focuses on the concepts and applications of traditional approaches to asset allocation, based on Mean-Variance Optimisation, with a focus on estimation risk and practical problems with Markowitz approach. This chapter also investigates the possible solutions to estimation errors: the additional weight constraints method and the resampling method, illustrating the application of resampling, the properties of resampled portfolios, and the discretionary choices in resampling. The chapter goes on with an in-depth analysis of a Bayesian strategic asset allocation method based on the Black-Litterman model, introducing the first information set of the model (equilibrium or implicit returns), the second information set (the views), the blending of information sets, and the application of the Black-Litterman model in practice.