A Modified Hierarchical Risk Parity Framework for Portfolio Management

Abstract

This article introduces a modified hierarchical risk parity (MHRP) approach that extends the HRP approach by incorporating three intuitive elements commonly used by practitioners. The new approach (1) replaces the sample covariance matrix with an exponentially weighted covariance matrix with Ledoit–Wolf shrinkage; (2) improves diversification across portfolio constituents both within and across clusters by relying on an equal volatility, rather than an inverse variance, allocation approach; and (3) improves diversification across time by applying volatility targeting to portfolios. The author examines the impact of the enhancements on portfolios of commodity trading advisors within a large-scale Monte Carlo simulation framework that accounts for the realistic constraints of institutional investors. The author finds a striking improvement in the out-of-sample Sharpe ratio of 50%, on average, along with a reduction in downside risk. TOPICS:Statistical methods, simulations, big data/machine learning Key Findings • The author introduces a modified hierarchical risk parity (MHRP) approach, which incorporates three popular portfolio management techniques into the hierarchical risk parity framework of Lopez de Prado. • He reports a striking improvement in the out-of-sample Sharpe ratio of 50%, on average, for portfolios of commodity trading advisors. • The author argues that the MHRP framework has broad applications for portfolios of traditional and alternative investments.