Dynamic estimation of sample covariance matrices via hierarchical clustering

Modern portfolio theory (MPT), proposed by Harry Markowitz, remains central to portfolio optimization, but its reliance on traditional covariance estimators limits its effectiveness in high-dimensional settings. This paper introduces a dynamic estimation with hierarchical clustering (DEH) model that combines rolling sample covariances with clustering-based structure to improve estimation stability and responsiveness. Using a panel of the 200 most liquid ETFs, we evaluate DEH across varying lookback lengths, rebalancing frequencies, and portfolio sizes. DEH consistently delivers stronger risk-adjusted performance, under monthly, quarterly and yearly rebalancing. In very large portfolios, DEH is still broadly competitive, but its performance becomes more dependent on the interaction between lookback length and rebalancing horizon, so its advantages are less clear-cut. Accordingly, explicit transaction-cost modelling and richer clustering specifications remain important directions for future research. These findings highlight DEH as a practical and interpretable tool for dynamic portfolio optimization in volatile and high-dimensional markets by effectively combining machine learning techniques with financial insights.