Johan Lim from Seoul National University.
Paper: High-Dimensional Markowitz Portfolio Optimization Problem: Empirical Comparison of Covariance Matrix Estimators
We compare the performance of recently developed regularized covariance matrix estimators for Markowitz’s portfolio optimization, the minimum variance portfolio (MVP) problem in particular. We focus on five estimators that are applied to the MVP problem in the literature, two of which regularize the eigenvalues of the sample covariance matrix while the other three assume sparsity of the true covariance matrix. The comparisons are made with two sets of long-term S&P 500 stock return data that represent two extreme scenarios of active and passive managements. The results show that the MVPs with sparse covariance estimators have a high Sharpe ratio, whereas the naive diversification (also known as uniform portfolio on market share) still works well in view of the wealth growth.