An Improvement of the Global Minimum Variance Portfolio Using a Black-Litterman Approach

Abstract

Asset management companies are constantly searching for portfolio optimization models that are on the one hand clear and intuitive and on the other provide high and reliable returns. This paper presents a modified version of the well-known Black-Litterman portfolio optimization approach. Unlike in the original model, the intuitive global minimum variance (GMV) portfolio serves as the reference portfolio. The introduction of a general rule for investors' views in combination with a simplification of the original Black-Litterman approach facilitates the implementation of the model and enables us to remove so-called dead assets from the GMV portfolio. As an additional advantageous feature our model is only based on variance-covariance estimations, and relative return estimations for our general rule. A numerical application of our modified Black-Litterman model to empirical data sets demonstrates that portfolios based on the model clearly outperform the GMV portfolio and the 1/N portfolio in terms of compound annual returns and out-of-sample Sharpe ratios.