In this paper, Derek Devens, CFA, Senior Portfolio Manager, Neuberger Berman Option Group and Doug Kramer, Co-Head of Quantitative and Multi-Asset Class investments discuss how they believe that there are two competing, and/or complementary, approaches to getting low volatility exposure – statistical and structural.

Decades ago, asset allocation was essentially limited to a mix of fixed income, equities, private investments and cash aimed at generating an annual return in excess of a fixed return target. Given that both equity and fixed income offered reasonable long-term rates of return in excess of the target and that cash earned returns more than zero, the opportunity cost of expressing a view of a few percentage points in favor of either equity or fixed income was manageable. In short, career risk didn’t preclude sound investment decision making and ‘savvy’ investors periodically shifted allocations to attempt to avoid steep declines in either bonds or equities and keep ‘dry powder’ for more attractive opportunities.

The Allocator’s Conundrum

Over the last decade, allocations have become far more complex, peer-awareness has increased substantially and prospective return differentials between asset classes appear more pronounced. Allocators are tasked with the notable challenge of remaining both fully invested and positioned to avoid excessive risk, while also earning a mandated rate or return. Hence the motivation is very high for investment teams to seek out investment opportunities that provide ‘equity-like’ returns with lower volatility than broad-based equity indexes, i.e., higher risk-efficiency and/or lower beta. However, the question remains, what is the best way to get low volatility exposure? We believe that there are two competing, and/or complementary, approaches – statistical or structural.

Over the course of this paper, we wish to highlight that investors allocating to traditional lower volatility equity strategies (statistical) are expressing explicit investment sector, style, factor, capitalization and interest rate biases. Consequently, we believe index put writing (structural) can offer a complementary strategy that lacks the dependence on backward looking relationships and limits the basis risk to broader equity indexes. Further, while both low volatility approaches may help address current investor fears about the potential for a stagnant or declining market, index put writing may derive greater benefit from other risk factors such as increases in market volatility or rising interest rates.

A Statistical Approach: Low Volatility Equity

The list of investment strategies that attempt to provide this return profile includes a variety of alternative investment strategies, but strategies that simply hold ‘low volatility’ equity portfolios are among the most widely accepted and have been for good reason. The table below provides a comparison of the S&P 500 Low Volatility Index (“SPLVI”) and the MSCI USA Minimum Volatility Index (“USMVI”) to their respective ‘full volatility’ parent index for the longest common period for which return data is available . The ‘low volatility’ equity indexes outperformed their full volatility parent indexes and, as designed, experienced lower monthly return volatilities and drawdowns.

Return & Risk Statistics

December 1990 – January 2017

Low Volatility

Source: Bloomberg LP.

Low volatility indexes like the S&P 500 Low Volatility Index (“SPLVI”) and MSCI USA Minimum Volatility Index (“USMVI”) follow statistical approaches to index construction. While each index has its own investment approach, philosophically they construct portfolios that hold equity securities that have expressed lower volatility over some backward looking timeframe. Look back time periods vary, but this philosophy assumes a degree of performance persistence.

Comparing a few relative statistics of the indices below also suggests that the low volatility indexes offer characteristics that investors might expect from traditional ‘active’ equity strategies. Hence, low volatility indexes and their related exchange traded funds (“ETFs”) typically fall under the industry’s ‘smart beta’ moniker. For a quantitatively focused industry, deciding what is or isn’t ‘smart beta’ is surprisingly subjective. We find it difficult to not view the use of the term as a sort of ‘active risk in disguise’, the sort Jacques Clouseau might dawn. After all, investing in both the S&P 500 Index and the S&P 500 Low Volatility Index is in effect simply overweighting a subset of stocks held in the S&P 500.

Low Volatility Equity Strategy Portfolio Statistics

December 1990 – January 2017

Low Volatility

*No ETF for MSCI USA.

Source: Bloomberg LP.

Many factors can reduce a stock’s return volatility, including high dividends, stable earnings, large market capitalization, low financial leverage and low share turnover, i.e., concentrated ownership. However, the systematic application of a rule set across any universe of stocks can lead to portfolio exposure imbalances, both intended and unintended. The charts below illustrate the relative market capitalization and sector exposures of the ProShares S&P 500 Low Volatility ETF (“SPLV”) and the iShares Edge MSCI Minimum Volatility USA ETF (“USMV”) versus the SPDRS S&P 500 ETF (“SPY”).

Market Capitalization (vs. SPY) (%)

December 31, 2016

Low Volatility

Source: Bloomberg LP.

GICS Sectors (vs. SPY) (%)

December 31, 2016

Low Volatility

Source: Bloomberg LP.

The charts make plain the biases inherent in both the SPLVI and USMVI and highlight the fact that MSCI’s index methodology imposes constraints on the minimum variance index’s relative exposures which was also illustrated by the lower tracking error and active share statistics in the previous table. Further, the tables below provide regression based return betas and factors for the indexes. As expected, the sector return betas align with the relative sector exposure presented above and the size factor exposure in the Fama-French factor analysis is consistent. The low volatility indexes’ biases towards small-cap value exposure may explain a reasonable portion of their relative performance success. Further, the sector concentration, while historically fruitful from a low volatility point of view, quietly embraces others risks that remain less obvious.

S&P 500 GICS Sector Betas

December 31, 2016

Low Volatility

Source: Bloomberg LP, Fama/French Data Dartmouth.

Fama-French Factor

December 31, 2016

Low Volatility

Source: Bloomberg LP, Fama/French Data Dartmouth.

As a potential byproduct of the exposures illustrated above, including the heavy Utilities and Consumer overweight, the low volatility indexes’ relative returns versus the S&P 500 Index appear to exhibit sensitivities to changes in interest rates, which we define as the 10-Year U.S. Treasury yield. For example, the scatter plots below chart the rolling 1-year excess return of SPLVI vs. the S&P 500 Index and USMVI vs. the MSCI USA Index against the rolling 1-year change in the yield on the 10-Year U.S. Treasury. It appears that the SPLVI has had a tendency to outperform in months when the 10-Year yield declined and underperform in months when the 10-Year yield increased.

S&P 500 Low Volatility Index vs. S&P 500

1-Year Excess Return vs. Change in 10-Year U.S. Treasury Yield. November 1990 – December 2016

Low Volatility

Source: Bloomberg LP.

MSCI USA Minimum Volatility Index vs. S&P 500

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