@article {Rudinjpm.2020.1.148, author = {Alexander Rudin and Vikas Mor and Daniel Farley}, title = {Adaptive Optimal Risk Budgeting}, elocation-id = {jpm.2020.1.148}, year = {2020}, doi = {10.3905/jpm.2020.1.148}, publisher = {Institutional Investor Journals Umbrella}, abstract = {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{\textendash}variance efficiency when Sharpe ratios and correlations of assets vary while retaining some of the ERC{\textquoteright}s robustness to estimation errors. The authors test AORB{\textquoteright}s viability by putting it in competition with ERC itself and with a version of the Bayesian shrinkage mean{\textendash}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 premiaKey Findings{\textbullet} 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).{\textbullet} When tested against popular Equal Risk Contributions and {\textquotedblleft}classical{\textquotedblright} 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.{\textbullet} AORB can also be trivially expanded to incorporate tail risk considerations.}, issn = {0095-4918}, URL = {https://jpm.pm-research.com/content/early/2020/03/06/jpm.2020.1.148}, eprint = {https://jpm.pm-research.com/content/early/2020/03/06/jpm.2020.1.148.full.pdf}, journal = {The Journal of Portfolio Management} }