Revisiting EWMA in High-Frequency Portfolio Optimization: A Comparative Assessment

This paper compares the statistical and economic performance of state-of-the-art highfrequency based multivariate volatility models with a simpler, widely used alternative - the Exponentially Weighted Moving Average (EWMA) filter. Using over two decades of 100 U.S. stock returns (2002–2023), we assess model performance through a Global Minimum Variance portfolio optimization exercise across various forecast horizons. We find that the EWMA model consistently outperforms more complex HF-based volatility models, delivering significant utility gains when including transaction costs, due in part to its lower turnover. Even in the absence of transaction costs, the EWMA filter cannot be beaten in most cases. Our results are robust to various dimensions, including no-short-selling constraints, varying portfolio sizes, and alternative parameter choices, highlighting the continued relevance of the EWMA model in high-frequency-based portfolio allocation.