Editor’s Letter

This issue of JAI begins our 16th volume. One might think that after all these years of analyzing the investment characteristics of alternative investments, we would have successfully answered all the pressing issues that matter to investors. Here is the bad news: We still have a long way to go to understand the dynamics of asset markets. Here is the good news: We still have a long way to go to understand the dynamics of asset markets.

In fact, it is the difficulty in fully understanding the dynamics of asset markets that offers all of us the return opportunities that exist in these markets. Without the risks embedded in our less than perfect understanding “even of perfect markets,” we and the assets we invest would have less perceived value, at least in terms of perceived risk and expected return.

In the following articles we attempt to continue our exploration of alternative investment. The first set of articles deals with issues in the risk and return assessment of basic commodities as well as commodity trading advisors.

In “The Performance of Simple Dynamic Commodity Strategies,” Devraj Basu and Joèlle Miffre construct real-time trading strategies based on the dynamic theories of Cootner [1960], Stoll [1979], and Hirshleifer [1990]. They construct the strategies using the aggregate positions of hedgers, and for a sample of 10 liquid commodities find broad support for these dynamic theories. The active long flat strategies outperform buy and hold strategies even during a commodity bull market, suggesting that these actively managed strategies are better investments than passive indexes. The results illustrate the importance of being able to capture “phases of backwardation” even during a commodity bull market.

Recently, considerable research has examined the impact of historical return patterns on the measured risk of both traditional and alternative investments. Central to this analysis is the industry’s failure to take into account the autocorrelation of a security or asset strategy when measuring the asset’s underlying risk or in estimating the current risk environment (Lo [2002]). As Galen Burghardt and Lianyan Liu show in “Autocorrelation Effects on CTA and Equity Risk Measurement,” the industry’s failure to take autocorrelation into account has produced estimates of equity volatility that are much lower than they should be and estimates of CTA volatility that are much higher. The impact of these results have implication not only for standalone performance measurement but also for portfolio based risk models, which increasingly form the basis for financial firms as well as individual portfolio risk assessment.

In “Scarcity, Risk Premiums, and the Pricing of Commodity Futures: The Case of Crude Oil Contracts,” Marco Haase and Heinz Zimmermann propose a simple decomposition of spot prices into a pure asset price plus a scarcity related price component, where risk premiums of commodity futures are directly related to the physical scarcity of commodities. This replaces the traditional convenience yield that results from an imperfect no-arbitrage relationship of the term structure of commodity futures prices. Their empirical tests confirm that two separate commodity-specific risk premiums affect the pricing of crude oil futures contracts: a net hedging pressure premium and a scarcity premium. The two premiums show different cyclical characteristics. They also find that asset market risk factors such as exchange rates or stock market shocks affect the term structure of oil futures prices in a much more homogeneous way than commodity-specific hedging pressure or scarcity shocks do.

The second set of articles deals with issues in the risk and return assessment of hedge funds and various hedge fund investment vehicles.

“Does Manager Offshore Experience Count in the Alternative UCITS Universe?” by Benoît Dewaele, Iliya Markov, Hugues Pirotte, and Nils S. Tuchschmid examines the performance of alternative UCITS funds on the basis of manager offshore experience and, additionally, the existence of an “equivalent” offshore hedge fund. Managers with offshore experience are defined as management companies managing offshore hedge funds in addition to managing UCITS. For a sample period from 2008 to 2011, the authors find that such UCITS have a positive alpha, with a P-value of 0.12 due to the limited size of the subsamples, which could provide some evidence of offshore manager added value. Among these UCITS, they identify further those that have an equivalent offshore hedge fund whose performance is replicated by using the same or a similar strategy, or through a swap. They also find that “offshore-experienced” UCITS without offshore equivalents (1) exhibit no meaningful differences in mean performance compared to those with equivalents, but (2) are generally less volatile and show a positive significant alpha at the 95% level. Concentrating then on those with equivalent offshore hedge funds, they find the onshore-offshore comparison shows no significant differences in mean performance and volatility when they use equally-weighted indexes, but an offshore outperformance when they perform a cross-sectional study. The authors also find a sizable regulation-induced tracking error.

Investors frequently use rolling regressions to estimate dynamic factor exposures. The estimation interval is an important parameter, but is often set unsystematically. In “Dynamic Hedge Fund Exposures: One Size Estimation Interval Doesn’t Fit All,” Brian Hayes studies interval selection through a model in which a fund’s factor exposure switches between high and low states, and investors track or clone this exposure with a rolling regression. The mean square error between the fund and clone depends on the fund’s exposure turnover and idiosyncratic risk, as well as the estimation interval. Lower idiosyncratic risk and higher turnover favor shorter intervals, but when turnover is sufficiently high, an expanding window is superior to fixed intervals. Consistent with his model, expanding interval clones of some popular hedge fund indexes have smaller errors than those based on rolling intervals of any length.

Many hedge funds attempt to achieve high returns by employing leverage. However, it is difficult to track the degree of leverage used by hedge funds over time because detailed, timely information about their positions in asset markets is generally unavailable. In “Hedge Fund Dynamic Market Sensitivity,” Jiaqi Chen and Michael L. Tindall discuss how to combine shrinkage variable selection methods with dynamic regression to compute and track hedge fund leverage on a time-varying basis. They argue that their methodology measures leverage as well as hedge fund sensitivity to markets arising from other sources. Their approach employs the lasso variable selection method to select the independent variables in equations of hedge fund excess returns. With the independent variables selected by the lasso method, a state space model generates the parameter estimates dynamically. The hedge fund market sensitivity indicator is the average of the absolute values of the parameters in the excess return equations. Their indicator peaks at the time of the Long Term Capital Management meltdown in 1998 and again at a critical time in the 2008 financial crisis. In the absence of direct information from hedge fund balance sheets, their approach could serve as an important tool for monitoring market sensitivity and financial distress in the hedge fund industry.

I suspect that each of the articles in this issue will offer both conclusions and controversy, but we welcome both. Remember, if it were easy, they could hire a monkey and feed it bananas.

We continue to look to all of our readers to offer their own insights into our ever-changing investment landscape.

Thomas Schneeweis

Editor

• © 2013 Pageant Media Ltd