It's common to hear of the value of diversification during uncertain or volatile markets. Indeed, a broadly diversified, balanced portfolio is unlikely to perform as poorly as a portfolio focused entirely on stocks, if stocks enter a bear market or experience seemingly abnormal volatility. Perhaps this is a primary reason the market environment since the recent global financial crisis has spawned such disappointment and a perception that diversification no longer works. For instance, since 2008, most risky asset classes have seemingly moved in lock step, with correlations to U.S. equities over the past three years ranging from 0.6 (for commodities) to 0.93 (for developed international markets). Indeed, only U.S. Treasury bonds have proven to be a true diversifier, correlating at -0.3 to U.S. equities.
Although carefully examining correlation is critical to the process of portfolio construction, great care must be exercised in using correlation as the foundation for a portfolio's construction. Correlation is a statistical measure, subject to estimation error, and correlations among assets can vary both over time and in different circumstances. And, as the recent market environment has shown, many risky assets can and do perform similarly during periods characterized by risk aversion and a general flight to quality.
So what can investors do with this information? How can they ensure that a portfolio is properly diversified? This article discusses what correlation does and does not mean for diversification, the implications of dynamic (that is, changing) correlations, the risk of relying on historical correlations during a flight to quality, and the benefit of focusing on fixed-income instruments as a source of consistent diversification benefit to mitigate the near-term risk of the equity markets.1
Defining Correlation
Correlation is a measure of the tendency of the returns of one asset to move in tandem with those of another asset. In other words, two assets that are "uncorrelated" could be expected to show no systematic, linear relationship between their returns over time. By combining uncorrelated assets, the movements of one asset can be expected to at least partially mitigate the movements of the second asset, reducing the average volatility of a portfolio. The first half of this paper examines the impact of correlations on portfolio construction and examines how correlations can change over time.
Although most investors have long-term investment goals, they are particularly averse to large losses, even over the short term. The second half of our analysis thus looks closely at what happens to correlations and, ultimately, diversification during periods of severe market stress. At such times, diversification benefits can seem to vanish among some assets with low long-term correlation, while the diversification benefits of other assets may become more apparent.
Setting The Baseline: What Does Correlation Tell Us?
Correlation is a statistical measurement used to convey the strength and direction of a linear relationship between two random variables. In finance, these variables can be anything from an individual security to an entire asset class. Increasingly positive (negative) correlation indicates an increasingly strong (inverse) relationship between the two variables, up to 1 (-1), which indicates a perfectly positive (inverse) relationship. In other words, two stocks with perfect correlation would be expected to move up and down in fixed proportion over a given period of time. Of course, because distinct investments are by definition influenced differently by the same factors, perfect positive correlation is extremely rare. For example, for the period from Jan. 1, 2000, through Dec. 31, 2011, the returns of ExxonMobil and Chevron—two very similar oil services firms—correlated at 0.85 on a daily basis, and 0.74 on a monthly basis (source: Thomson Reuters Datastream). Although the two companies moved in the same direction on 2,541 days, they moved in opposite directions on 589 days.
Even in the case of a preannounced stock-for-stock merger of two corporations (in which the equity of one entity will be converted into equity of another in fixed proportion at a given future date), correlations can be less than 1.0. And while correlation conveys information about tendencies in the direction of the change in value of two investments, the statistic itself conveys very little information about the absolute level of change in value of the assets. For example, over the same period, ExxonMobil posted a 110 percent cumulative return, while Chevron notched a more impressive 146 percent cumulative return. So despite the companies' high correlation, investing in one was not "just as good" as investing in the other. In fact, investors must be equally aware of the things that correlation does not tell them.
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