Recent Research: Highlights from June 2011
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“Futures-Based Commodity ETFs.” The Journal of Index Investing (Summer 2011). Ilan Guedj, Guohua Li, and Craig McCann.
Exchange-traded funds (ETFs) have become popular investments since first introduced in 2004. These funds offer investors a simple way to gain exposure to commodities, which are thought of as an asset class suitable for diversification in investment portfolios and as a hedge against economic downturns. However, returns of futures-based commodity ETFs have deviated significantly from the changes in the prices of their underlying commodities. The pervasive underperformance of futures-based commodity ETFs compared to changes in commodity prices calls into question the usefulness of these ETFs for diversification or hedging. This article examines the sources of the deviation between futures-based commodity ETF returns and the changes in commodity prices using crude oil ETFs. The authors show that the deviation in returns is serially correlated and that a significant portion of this deviation can be predicted by the term structure of the oil futures market. They conclude that only investors sophisticated enough to understand and actively monitor commodity futures market conditions should use these ETFs.
This article examines the impact of the “Lost Decade“of stock returns (2000–2009) on the projected performance of recent long-term stock investors. While that decade impacted everyone who remained invested during those years, it is particularly devastating to the future performance of investors who began investing in stocks around 2000 and plan to remain invested for periods lasting up to another 30 years. Recovery from the “Lost Decade“ depends not only upon both future market returns and the investment horizon, but also upon how an investor defines recovery. While recovery to a rate of return most investors seem to expect from stocks is possible, an examination of the likely probabilities of future rates of return indicate that the chances are small in most cases. As always, luck plays a role, and a limited analysis of differing starting dates reveals how final wealth can be affected by small temporal changes.
“Public or Private? A Review of the Eclipse of the Public Company in the Current Environment.” The Journal of Private Equity (Summer 2011). Joseph W. Bartlett.
The roster of publicly listed companies in the U.S. is in a steep decline—their “eclipse” the result of multiple causes. Nature abhors a vacuum, so the attractiveness of remaining private, with liquidity provided by secondary trading platforms, is growing remarkably. The root cause of the eclipse is discussed in a surprising piece by one of the chief stewards of shareholder rights, Delaware Vice Chancellor Leo Strine, shining the light on institutional investors who dominate trading on the NYSE and NASDAQ and whose strategy is akin to that of day traders. The issue discussed in the article, with help from Marty Lipton’s insights on “activists,“ is how to fix the system so as to reward patient investors in public companies with extended time horizons.
“What Does Implied Volatility Skew Measure?” The Journal of Derivatives – Summer 2011 vol. 18 no. 4. Scott Mixon.
The Black–Scholes model has been acknowledged as a brilliant breakthrough in asset pricing theory. But in applying it to real world options, problems immediately arose, because the volatility that make an option’s model value consistent with its market price is different for different strike prices: the wellknown “volatility smile.“ Over time, the smile evolved into a more monotonic, downward-sloping “skew,“ and traders became comfortable with the idea of modeling its behavior and describing option market conditions in terms of the level and skew of implied volatilities. A standard explanation for the skew is that the return distribution is not lognormal; in particular, it generally has a negative third moment (i.e., negative skewness). The similarity of the terms and the (potential) connection between the volatility skew and statistical skewness is one source of confusion. Another is that (unlike skewness) there is no standard measure for the volatility skew. Mixon explores these issues and reviews a number of common skew measures. One significant result is that most of them vary strongly with the level of volatility, making comparisons across different underlying assets or over time difficult. After examining several performance measures, Mixon suggests that the most useful measure of the volatility skew is the difference between the implied volatilities for a 25 delta put and a 25 delta call, divided by the implied volatility for a 50 delta option.