Today market participants understand that many sources of risk and return can be sharply focused into an index and used to measure a unique return pattern over a market cycle. But with the proliferation of these “smart beta” indexes there is potential for confusion. This is especially true when two indexes appear to have the same goal at first glance. Such is the case with low volatility and minimum variance indexes.
Think of a bottom-up stock picker’s approach to portfolio construction. He assesses each stock individually based on a desired set of characteristics. He then overweights those stocks that score high on that set of characteristics and underweights or completely excludes those stocks that score low. That is the essence of a low volatility index, even though there is no actual stock picker, as an index is completely rules-based. The sole characteristic the index rules target is the return volatility (standard deviation) of each stock. Stocks with low volatility are emphasized while those with higher volatility are de-emphasized or not included at all.
By selecting stocks individually, the low volatility index rules maximize the capture of the low volatility “factor.” There is a body of research suggesting that this factor may generate excess returns relative to a cap-weighted index over a market cycle.1 Potential concerns are that in pursuing strong factor capture, unintended tilts to size, sector, country and illiquidity can occur. Also, a low volatility factor index can have an overall volatility that is only slightly smaller than its cap-weighted parent index.
Now think of a top-down “quant’s” approach to portfolio construction. She uses an optimizer to achieve a whole portfolio with desirable properties. The properties might include targets in risk, return, sector exposures, country exposures and more. She is not very concerned with what stocks the optimizer actually selects; she is only focused on whether the portfolio targets are achieved. This is essentially the approach of a rules-based minimum variance index.
The sole criterion of the index optimizer is the variance (squared volatility) of the whole index return. This has often resulted in the index having smaller drawdowns during market corrections. By taking a whole index approach, a minimum variance index takes into account the correlations between stocks. But because correlations are considered, some of the lowest volatility individual stocks may not be included in the minimum variance index. Even so, there still is enough capture of the low volatility factor to support a return objective that is larger than the theoretical minimum variance portfolio, illustrated by the chart. Also, an optimizer can easily impose constraints that limit unintended tilts to maintain balanced exposures to the market.
In choosing between these two types of indexes, it should be kept in mind that they both lower volatility, albeit to different degrees, and they both capture the factor premium, but also to varying degrees. The low volatility index has strong factor capture and it easily combines with other factor indexes constructed with a similar methodology, such as value, momentum and quality. The minimum variance index focuses on achieving the lowest overall volatility possible while maintaining a balanced exposure to the market. Clearly, having both of these indexes available increases market participants’ choice and flexibility.
Read more on the low volatility versus minimum variance indexes discussion.
 See the discussion and references in “Low Volatility or Minimum Variance: An ‘Eyes Wide Open’ Discussion,” FTSE Russell, 2015.
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