RT Journal Article SR Electronic T1 The Devil Is in the Details: The Risks Often Ignored in Low-Volatility Investing JF The Journal of Portfolio Management FD Institutional Investor Journals SP jpm.2020.1.163 DO 10.3905/jpm.2020.1.163 A1 Nicholas Alonso A1 Oleg Nusinzon YR 2020 UL https://pm-research.com/content/early/2020/05/20/jpm.2020.1.163.abstract AB With increasing investor interest in low-volatility equity strategies comes a need for greater scrutiny of different methodologies used to achieve low-volatility exposure. In an earlier article, the authors investigated the analytical differences between a variety of approaches to constructing low-volatility portfolios. In this article, the authors turn their attention to the empirical differences between common approaches to low volatility. They find that a traditional optimizer-based approach to building low-volatility portfolios has large sensitivities to the risk inputs used in the process. In fact, using the same portfolio construction methodology but changing the risk inputs even slightly can lead to large differences. The magnitude of this sensitivity should give investors pause; even across risk inputs in which differences are valid, variations persist and can be harmful to portfolio performance. The authors show that there are other, more robust, ways of achieving low-volatility portfolios without this input sensitivity (e.g., risk balancing) and suggest that investors should consider this lack of input sensitivity as a valuable characteristic in low-volatility investing.TOPICS: Volatility measures, exchanges/markets/clearinghouses, risk managementKey Findings• Using an optimizer to gain exposure to the low-volatility anomaly/premiums subjects the portfolio construction process to input sensitivity, which can lead to wide variation in results.• Input sensitivity affects not only the portfolio’s sector and stock positioning but also its risk exposures. This variation in risk exposures can lead to very different outcomes for investors.• There are more robust ways to gain exposure to the low-volatility anomaly/premiums, and the authors show that a risk-balanced approach not only captures the anomaly/premiums well but does so under different risk model assumptions.