The large drawdowns experienced during the financial crisis have spawned a wide array of indices that seek to limit drawdowns by controlling volatility.  S&P Dow Jones Indices offers a suite of Risk Control Indices that seek to target a predetermined level of volatility by shifting allocations between the underlying index and cash.

I had looked into this methodology a bit over the summer since I was curious as to how successful this approach is at targeting the a specific volatility over time rather than merely on average.  The following graphs show the results of targeting a 10% annual volatility through time for a selection of ETFs using a 10% rebalance threshold to reduce turnover and capping the maximum leverage at 150%, as is common in these indices.

DBC Target vol TLT Target vol SPY Target vol RWR Target vol EEM Target vol

All in all, this methodology appears to do a good job of targeting the 10% volatility level through time, even for RWR when its volatility spiked to over 90% during the crisis  The table below shows the average volatility for these ETFs/indices (and more) along with the volatility of the volatility (vol of vol).  The vol of vol paints a picture of how much oscillation we have around the target value of 10%.  But what is the performance like of these controlled volatility indices?  The table also presents the return, Sharpe ratio, and drawdown characteristics for each ETF and index.

 Annualized VolatilityVol of volAnnualized ReturnSharpe RatioMax DrawdownReturn to Max DD
AGG5.4%2.5%4.3%0.8012.8%0.33
AGG 10% Target Volatility6.0%1.7%5.0%0.8313.3%0.38
DBC22.0%6.2%1.7%0.0860.3%0.03
DBC 10% Target Volatility9.2%0.8%2.6%0.2820.8%0.12
EEM33.1%14.4%14.6%0.4466.8%0.22
EEM 10% Target Volatility9.4%1.3%6.5%0.7016.2%0.40
EFA24.2%9.5%6.0%0.2561.8%0.10
EFA 10% Target Volatility9.3%1.1%3.9%0.4223.5%0.17
GLD20.9%5.3%11.6%0.5537.2%0.31
GLD 10% Target Volatility9.6%1.1%6.9%0.7222.3%0.31
JNK16.8%9.4%7.6%0.4538.1%0.20
JNK 10% Target Volatility8.7%1.9%4.9%0.5722.1%0.22
RWR34.1%20.2%8.7%0.2575.2%0.12
RWR 10% Target Volatility9.3%1.3%5.9%0.6423.7%0.25
SPY19.4%7.7%8.9%0.4655.2%0.16
SPY 10% Target Volatility9.3%1.0%6.3%0.6725.0%0.25
TLT14.0%4.0%6.2%0.4426.6%0.23
TLT 10% Target Volatility9.4%0.7%4.8%0.5114.4%0.33

These results are encouraging in that for each pair, targeting the 10% volatility led to a higher risk adjusted return.  The volatility targeted indices also had higher return to max drawdown ratios (except for GLD, which was flat on this).

One problem with this approach is that it will underperform in volatile increasing markets because it will be underweight the index.  The chart below shows that targeting volatility for SPY led to underperformance after each major drawdown while volatility was still high.

SPY Growth

One way to mitigate these problems is to use refined volatility estimates that are more forward looking.  However, the prevalence of realized volatility as the estimate for volatility indicates that forecasting volatility is difficult.

Another possible solution is tacticality.  BNY Mellon stated in their paper, Volatility Targeting May Miss the Mark, that targeting volatility has worked well in the past two decades because the low volatility factor has performed well (i.e. returns and volatility have been negatively correlated), but it has not outperformed when the lookback period is extended to 1950.  Thus, adding a tactical tilt to this strategy could reduce the implicit costs of underperforming in up markets while preserving the benefits of drawdown protection during down markets while keeping the goal of overall volatility management.

Nathan is a Vice President at Newfound Research, a quantitative asset manager offering a suite of separately managed accounts and mutual funds. At Newfound, Nathan is responsible for investment research, strategy development, and supporting the portfolio management team.

Prior to joining Newfound, he was a chemical engineer at URS, a global engineering firm in the oil, natural gas, and biofuels industry where he was responsible for process simulation development, project economic analysis, and the creation of in-house software.

Nathan holds a Master of Science in Computational Finance from Carnegie Mellon University and graduated summa cum laude from Case Western Reserve University with a Bachelor of Science in Chemical Engineering and a minor in Mathematics.