Optimal Portfolio Choice with Dynamic Asymmetric Correlations and Transaction Constraints

Abstract

This paper develops a framework for constructing portfolios with superior out-of-sample performance in the presence of estimation errors. Our framework relies on solving the classical mean-variance problem with dynamic portfolio rebalancing at a comparatively-high frequency level. With the employment of A-DCC GARCH model, we found that the usage of turnover constraints will tend to enhance the performance of the portfolios sufficiently high to overcome transaction costs in practice. For a long-only optimal portfolio based on a linear combination of two different strategies we find a return exceeding 51% per annual with annual volatility equal to 35% over the 1998-2007 period. We argue that the advantage of our framework comes from the mean-reverting nature of the stock market and the impact of the estimation errors in high frequency level. Our works indicate that one can successfully move from ordinary monthly or weekly adjusting strategies to high frequency and dynamic asset management without the significant increase of transaction costs.