Asset Allocation Strategies Using Covariance Matrix Estimators

Abstract

Abstract The covariance matrix is an important element of many asset allocation strategies. The widely used sample covariance matrix estimator is unstable especially when the number of time observations is small and the number of assets is large or when high-dimensional data is involved in the computation. In this study, we focus on the most important estimators that are applied on a group of Markowitz-type strategies and also on a recently introduced method based on hierarchical tree clustering. The performance tests of the portfolio strategies using different covariance matrix estimators rely on the out-of-sample characteristics of synthetic and real stock data.