An Empirical evaluation of alternative asset allocation policies for emerging and frontier market investors in Africa

Abstract

Despite forming an integral part of literature and practitioner knowledge, Markowitz based optimization has been shown to suffer severe drawback of estimation errors and sensitivity to input parameters when implemented in practice. The best diversification methods from the perspective of a private investor in real-life situations still remains largely unsolved. Most of the potential diversification benefits so far have primarily been analyzed for internationally diversified stock portfolios, with focus on the special viewpoint of U.S investors. Studies have suggested that the Mean-Variance optimization can be robustified by the use of robust covariance estimators other than the sample covariance that relies on the classical Maximum Likelihood Estimator. Using a portfolio formed from 2 Emerging Market and 5 Frontier Market indices in Africa, this study sought to compare the performance of the traditional Mean-Variance model against the performance of the Mean-Variance optimization model robustified with the Orthogonalized Gnanadesikan-Kettenring, Minimum Covariance Determinant, Minimum Volume Ellipsoid and shrink estimators, with an aim of recommending the best model applicable to the African emerging and frontier markets investors. The robustified models were found to indeed have better characteristics in terms of gross returns, annualized returns and net portfolio returns over time compared to the traditional Mean-Variance optimization model.