There’s a large body of research demonstrating that, while past returns do not predict future returns, past volatility largely predicts future near-term volatility.
For example, since 1926, the correlation of returns between adjacent 60-trading-day periods is essentially zero. In contrast, the correlation rises to 0.55 for risk as measured by standard deviation. Evidence that past volatility predicts future volatility has been found not only in stocks, but in bonds, commodities and currencies.
Based on evidence that shows they improve portfolio efficiency, managed volatility strategies have been developed to adjust exposures in inverse relation to a risk estimate.
The aim of managed volatility strategies is to stabilize realized portfolio volatility through time and produce a more efficient portfolio with less downside risk.
Funds such as AQR’s Style Premia Alternative Fund (QSPRX), AQR’s Managed Futures Strategy Fund (AQMRX) and the Stone Ridge All Asset Variance Risk Premium Fund (AVRPX) have been successfully managing portfolios to a targeted volatility for a number of years now—increasing leverage and exposure when volatility is falling and decreasing them when volatility is rising. (Full disclosure: My firm, Buckingham Strategic Wealth, recommends AQR and Stone Ridge funds in constructing client portfolios.)
Using Volatility To Mitigate Risk
Anna Dreyer and Stefan Hubrich contribute to the literature on managed volatility strategies through their November 2017 paper, “Tail Risk Mitigation with Managed Volatility Strategies.”
Their study covered the period 1926 through 2016. The authors found managed volatility (MV) strategies that dynamically alter their daily equity market exposure according to the ratio of realized past long-term equity volatility to trailing 20-day realized volatility (the former represents the volatility target; the latter represents the forecast) have “consistently hugged the target volatility within a much tighter range than the benchmark itself.”
Dreyer and Stefan Hubrich write: “This observation holds even during turbulent market episodes like the mid-1970s, the 1987 crash, the tech bubble/crash in the early 2000s, and the Global Financial Crisis (GFC) in 2008-09.” More specifically, they found 90% of their realized volatility observations for MV fell into the 0.68% to 1.59% interval, while the range for the benchmark was much wider, at 0.41% to 2.04%.
Reduced volatility and less downside risk are clearly important both to risk-averse investors and to all investors who are in the withdrawal stage, when the sequence of returns can matter greatly. Reduced downside risk can also help investors prone to panicked selling, which generally increases in severe bear markets.
While MV strategies are related to time-series momentum, they don’t perform similarly in all environments. However, realized volatility and returns tend to be negatively correlated.
For example, Dreyer and Hubrich found that the correlation for 60-day windows in their sample is -0.25—volatile periods tend to be associated with “bad news” and market sell-offs. In other words, volatility tends to spike up, not down.
Note that if market returns are positive, the use of leverage allows MV strategies to capture enhanced returns when volatility is low. By reducing leverage when volatility is high (and when returns tend to be poor), MV strategies tend to avoid the worst of bear markets.
The research shows that Sharpe ratios (SRs) are lower (higher) when volatility is higher (lower). This enabled Dreyer and Hubrich to conclude that MV strategies are not only related to time-series momentum, but also to returns’ long-term mean reversion.