Did Low Volatility Indices Do Their Job?

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Did Low Volatility Indices Do Their Job?

2019-11-15T13:23:25-05:00By |

This article will endeavor to look at the effectiveness of indices that target low volatility during the spike in equity volatility that occurred in the first 8 trading days of February (02/01/18-02/12/18) and how effective an equity risk parity strategy – whose goal is to leverage lower volatility assets in order to achieve better risk adjusted returns in the long run – would have been in protecting one’s portfolio during this equity correction. There are a few caveats associated with this analysis that I want to point out up front. First, “risk parity strategy” is a very broad term that can encompass many different asset classes. In this article I will be focusing specifically on the effectiveness during this period, of leveraging lower volatility equities with the goal of better risk adjusted returns. Second, since I am looking at the most recent correction, by definition, I will be looking at a very small sample size with specific unique circumstances and will need be cognizant of that when drawing larger conclusions.  Finally, I will be assessing if the logic of selling equities into volatility spikes is sound, or if instead, selectively buying into volatility is a path to boost returns.

Setting the Scene

Figure 1

As seen in Figure 1 above, on 1/31/2018 the S&P 500 Total Return Index (S&P 500 TR) closed the first month of the year, up a very strong +5.75%. Over the next six trading days, the S&P 500 TR would decline -8.55% (YTD performance becoming -3.31% as of 2/8/18), before gaining +2.94% over the following two trading days and settling at a YTD performance of -0.47% on 2/12/18.

The velocity of this movement surprised an equity market that had seen a trend of increasingly lower volatility since Q4 of 2015 and caused the CBOE Volatility Index (VIX) – a key measure of S&P 500 short term volatility – to spike to levels not reached since November of 2011.

A Look at Low Volatility Indices and Equity Risk Parity

In order to assess if low volatility targeted indices performed as intended during this particular market correction, I looked at a selection of low volatility indices tied to a variety of benchmarks, but all with the same general goal. See below for a brief description of the indices chosen:

SSGA US Large Cap Low Volatility Index – Index of least volatile Russell 1000 Index securities.

Fidelity Low Volatility Factor Index Index of large-cap US stocks selected for low volatility of returns and earnings.

JPM U.S. Minimum Volatility Index – Holds lower-volatility US large-cap stocks from the Russell 1000 Index, weighted to emphasize lower-volatility sectors.

MSCI USA Minimum Volatility Index –  Index of US-listed firms selected and weighted to create a low-volatility portfolio

S&P 500 Low Volatility Index – Weighted index of the 100 least-volatile stocks in the S&P 500

As shown in Figure 2 below, true to the intention of the indices, all five slightly outperformed the S&P 500 during the time period in question (average outperformance of 0.27%) while posting lower annualized volatility figures.

Figure 2

However, as discussed, the aim of a risk parity strategy would be to leverage lower volatility assets in order to achieve better risk adjusted returns. Therefore, I looked at a simplified example in which the low volatility indices were levered such that the trailing one-month daily annualized volatility was brought to parity with the S&P 500, the results of which can be seen below in Figure 3.

Figure 3

These five indices, which all outperformed in terms of risk and return on an unlevered basis, underperformed (by an average of -0.93%) when levered to match the volatility of the S&P 500 (based on trailing one-month daily annualized volatility). Does this conclude that an equity risk parity strategy would not have worked during the market correction in question? No. As I mentioned, this is one particular and very simple example. However, it does raise a couple of important concerns about risk parity in general.

First, risk parity is inherently backward looking, meaning there is no way to know if the “low-vol” constituents of the past month, year, or decade will react in a less volatile way to a future crisis. Second, the reliance on leverage in this strategy is essentially taking risk in the form of volatility and shifting it into risk in the form of leverage. Whether that shift in risk is beneficial to the investor is largely determined by the directionality of the market in which the increase in volatility takes place. If the spike in volatility occurs during the course of a strong up market, the leverage will amplify portfolio returns and the subsequent sell-off of the “now-more-volatile” returns (due to a risk parity driven deleveraging), would be an effective way to sell into strength and lock in profits. However, if the volatility is brought on by a market correction (as it was during the period in question), not only will the leverage magnify the negative returns but the risk parity deleveraging that follows would dictate selling into an already down market.

Historical Volatility Spikes

The Cboe Volatility Index® (VIX® Index®) is a key measure of market expectations of near-term volatility conveyed by S&P 500 stock index option prices. Since its introduction in 1993, the VIX Index has been considered by many to be the world’s premier barometer of investor sentiment and market volatility. Figure 4 below shows the VIX Index plotted against the S&P 500 from 1/1/2008 to 12/12/2018. This period was chosen to include both the 2008 recession and the recent 2018 correction.

Figure 4

As the red circled areas in Figure 4 clearly highlight, the largest VIX spikes predominantly peak during S&P 500 lows. A risk parity driven strategy would then be selling into a market largely doing the same and perhaps exacerbate the problem. A 2014 Barron’s article authored by Richard H. Wiggins Jr and Radi Alzayer concisely summed up this effect:

“Leaving a concert or ballgame 15 minutes early to avoid traffic doesn’t work when everybody else is thinking the same thing… Too many people pursuing risk parity—potentially trying to sell the same levered assets at the same time—may themselves be the source of the risk that they were hoping to avoid.”

Reducing one type of risk is not the same as reducing overall risk. It is therefore fair to raises the question – Is selling off assets as their volatility increases is a viable strategy? Especially when the graph above would indicate that buying at points of particularly high volatility would have been far more beneficial to the investor historically.

Is Volatility Bad?

It is prudent to diversify one’s portfolio to mitigate risks, however, if one diversifies away too many factors, then eventually the portfolio risks becoming the market, sacrificing any chance at outperformance. While volatility tends to spike highest in times of market stress, up-side volatility does exist and can in fact be beneficial to investors. In those situations, heightened volatility is a ”risk” that yields higher returns. The positive correlation between volatility and return is an integral part of the size factor (outperformance of Small Cap over Large Cap) perhaps most famously discussed by Eugene Fama and Kenneth French in their seminal research on the three-factor model1. Recently, investment firm AQR published an excellently titled academic paper called Size Matters, if You Control Your Junk to examine the apparent diminished expression of the size factor in the current market environment. AQR found the size factor still indeed yields outperformance – when selectively applied to “quality” companies (where quality is a characteristic or set of characteristics of a security that investors are willing to pay a high price for, all else equal).

One such strategy that exhibits the higher risk/return characteristics of Small-minus-Big is the Reverse Cap Weighted U.S. Large Cap Index. This S&P Dow Jones calculated Index, takes the constituents of the S&P 500 and weights them by the inverse of their market caps. The resulting portfolio gives more weight to the stocks on the lower end of the large-cap spectrum, which are underrepresented in most cap-weighted strategies. The fact that even the smaller constituents are still large cap names with the gravitas of being included in the S&P 500 insulate them from some of the informational and liquidity concerns that plague Small- and Micro-Cap names and is perhaps a method of exhibiting the “quality” that the AQR paper noted as being necessary for the size premium to exist.

Figure 5

As seen in figure 5, the Reverse Cap Index has historically shown to deliver a significantly higher annualized return than the Market Cap Weighted S&P 500, while sporting significantly higher volatility as well. The fact that Reverse has a higher Sortino Ratio (which measures return per unit of downside risk), demonstrates that a larger part of that higher volatility is in fact upside-volatility and good for investors. Just as a risk parity driven, systematic selling of equities when volatility spikes (selling when equities are relatively lower) and leveraging up assets when volatility comes down (buying when equities are higher) seems counter intuitive when taken out of the generally accepted framework of risk parity; The same counter-intuitive “buy high” and “sell low” traits are exhibited with traditional market cap weighting indices which allocated money to large institutions simply because they are large. The Reverse Cap Index instead systematizes “buy low”, “sell high” through its quarterly rebalances which sell winners and buys losers within the S&P 500 constituents.

Conclusions

Lower volatility Indices did in fact outperform that S&P 500 during the spike in volatility that occurred between 2/1/2018 and 2/12/2018. However, the idea behind using these lower volatility indices in a risk parity strategy would not have delivered better returns during this period and the above analysis raises questions about a strategy built on the general avoidance and selling of volatility. Volatility has garnered a reputation of something to be avoided in a portfolio, however selectively investing in higher volatility markets and securities has the potential to also add excess return to a portfolio.

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1Fama, Eugene L., and French, Kenneth,” The Cross-Section of Expected Stock Returns”, Journal of Finance, pp 427-465.

This document is for informational purposes only does not constitute an offer to sell or the solicitation of an offer to buy any security or investment product and should not be construed as such. Past performance does not guarantee future results.

The Reverse Cap Weighted U.S. Large Cap Index (REVERSE) is a rules-based reverse capitalization weighted index comprised of the 500 leading U.S.-listed companies as measured by their free-float market capitalization contained within the S&P 500 universe. The Index seeks to provide exposure to the smaller-end of the U.S. Large-cap market. The Index has an inception date of October 23, 2017, with a backtested time-series inception date of September 28, 2007.

It is not possible to invest directly in and index.

The Reverse Cap Weighted U.S. Large Cap Index  (the “Index”) is the property of Exponential ETFs, which has contracted with S&P Opco, LLC (a subsidiary of S&P Dow Jones Indices LLC) to calculate and maintain the Index. The Index is not sponsored by S&P Dow Jones Indices or its affiliates or its third party licensors (collectively, “S&P Dow Jones Indices”). S&P Dow Jones Indices will not be liable for any errors or omissions in calculating the Index. “Calculated by S&P Dow Jones Indices” and the related stylized mark(s) are service marks of S&P Dow Jones Indices and have been licensed for use by ACSI Funds. S&P® is a registered trademark of Standard & Poor’s Financial Services LLC (“SPFS”), and Dow Jones® is a registered trademark of Dow Jones Trademark Holdings LLC (“Dow Jones”).

All information for an index prior to its Launch Date is back-tested, based on the methodology that was in effect on the Launch Date. Back-tested performance, which is hypothetical and not actual performance, is subject to inherent limitations because it reflects application of an Index methodology and selection of index constituents in hindsight. No theoretical approach can take into account all of the factors in the markets in general and the impact of decisions that might have been made during the actual operation of an index. Actual returns may differ from, and be lower than, back-tested returns.

Posted by: Josh Blechman 02/27/2018

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