As market volatility continues unabated, investors across the globe are seeking out tools, strategies and solutions to reduce their overall risk while still reaping the rewards that equity investing can provide. Low-volatility strategies have become valuable tools for many investors and advisors, but are all low-volatility strategies created equal?
We recently hosted two experts from S&P Dow Jones Indices – Craig Lazzara, Global Head of Index Investment Strategy and Shaun Wurzbach, Global Head of Financial Advisor Channel Management – for an in-depth conversation about low-volatility investing.
Part one, Q&A: Understanding low-volatility investing, introduced the foundations of low volatility, how it was developed and how it works.
The following is part two of the three-part conversation. Stay tuned for the third installment covering implementation ideas that demonstrate the potential roles low-volatility can play in a portfolio.
Chris: Are all low-volatility indices created in the same way?
Craig: No, they are not. There are two basic ways to produce a low-volatility portfolio. The one generally referred to as the “minimum variance approach” is the older of the two. The second is a rankings-based, unconstrained approach that measures volatility using standard deviation. A minimum variance approach seeks to produce an index with the lowest absolute volatility, but within a given set of constraints, never straying from its parent index beyond those constraints.
The unconstrained, rankings-based approach doesn’t have any sector or geographic restraints – meaning it can change dynamically based on the most current information and may stray from its parent index as volatility dictates, rather than as index constraints dictate. It effectively focuses on pure low-volatility stocks. The S&P indices used by PowerShares Canada all utilize an unconstrained, rankings-based approach.
Chris: How have performance returns differed between the two approaches?
Craig: Five-plus years ago when we did the research that led to the creation of the S&P 500 Low Volatility Index, we found that although over time the two approaches gave fairly similar results, where they differed is in the patterns of return.
In times when the market is under increased stress, a rankings-based approach tends to perform better. The outperformance is not directly due to the rankings, but rather a result of the constraints on the minimum variance solution. The minimum variance approach is often referred to as “constrained optimization” and, in my opinion, the most important thing to consider is not the optimization, but the constraints.
Chris: Can you provide specific examples?
Craig: Absolutely. In the U.S. between 2000 and 2002, technology made up somewhere between 25% and 30% of the S&P 500. At that time, a constrained minimum variance approach would have required a portfolio to hold 20% to 25% in tech as the sector fell, whereas a rankings-based approach would have been able to avoid the tech sector altogether.1
The same applies to the financial crisis in 2008. A constrained, minimum-variance optimizer would have led to a roughly 15% weighting in financials as they tumbled, while the rankings-based approach had roughly a 3% weight in the declining sector.1
It’s this ability to get out of the way of sectoral downturns that, historically, has made the rankings-based approach a better performer on the downside.
Now, this also means that the constrained approach will likely do better in rising markets. One of the things that we considered heavily during our research was that if investors are interested in low-volatility strategies because they want protection from down markets, let’s opt for the approach that does just that.
Shaun: Ultimately, after all of our research, we decided on a selection and weighting strategy because of the simplicity and transparency it provides.
Chris: What are the advantages and/or shortcomings of using standard deviation as opposed to beta as a driver of volatility?
Craig: We looked at both approaches when we started our research and it’s fair to say that they’re closely related. To be honest, that we ended up opting for standard deviation rather than beta was a kind of a surprise, even to us, because at the time we were also working on the S&P 500 High Beta Index – and some of us felt it would make sense if the approaches were symmetric. But it didn’t work out and the research and the data showed clearly that using standard deviation better mitigated volatility.
Chris: Can you explain the reasons behind that finding?
Craig: Well, some of the holdings in a low-volatility portfolio and a low-beta portfolio would certainly be the same. But, what might be different? In a low-beta portfolio you would find stocks without a lot of relation to the market as a whole, but they may have considerable unique or idiosyncratic risk. This makes the low-beta portfolio potentially riskier because a standard deviation focus aims to reduce volatility overall, without regard for the source – markets-related or idiosyncratic.
Alternatively, a low-beta approach is focused only on minimizing market-related volatility. This means portfolios based on low-beta criteria could include stocks with high idiosyncratic risk despite their low beta, or miss out on very low-volatility stocks because they have average beta.
Our extensive research showed that the unconstrained approach using standard deviation provided transparency, simplicity and true low-volatility that take into account all types of risk.
The next, and final, installment of my conversation with Craig and Shaun will cover how low-volatility strategies can provide the two Ps – participation and protection from downside risk – and portfolio construction ideas that investors and their advisors can consider.
If you have any comments or questions, please leave them in the comment area below.