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Adaptive Optimal Risk Budgeting

In this article, the authors suggest Bayesian-style adaptive enhancement to a popular equal risk contribution (ERC) portfolio construction technique they call adaptive optimal risk budgeting (AORB). The enhancement has the potential to bring portfolios closer to mean–variance efficiency when Sharpe ratios and correlations of assets vary while retaining some of the ERC’s robustness to estimation errors. The authors test AORB’s viability by putting it in competition with ERC itself and with a version of the Bayesian shrinkage mean–variance technique in a carefully simulated setting. They find that the new method appears to deliver measurable advantages over its competition in a broad range of realistic settings. Multiple possible applications to portfolios of risk premia strategies and a multi-asset universe more generally are discussed by the authors. TOPICS: Portfolio construction, analysis of individual factors/risk premia Key Findings • We suggested a computationally simple yet powerful portfolio construction approach that is formulated in terms of risk contributions but is designed to prescribe an approximately mean-variance efficient solution even when Sharpe ratios and correlations between assets vary in magnitude and over time. We called the new method Adaptive Optimal Risk Budgeting (AORB). • When tested against popular Equal Risk Contributions and “classical” Bayesian shrinkage methods, AORB was competitive in all cases and emerged as a clear winner when Sharpe ratios and correlations between assets were substantially differentiated. • AORB can also be trivially expanded to incorporate tail risk considerations.