The interest in timberland investing has grown over the years as investors have looked to gain exposure to alternative asset classes to enhance returns and diversify their traditional market exposures. In this article we provide a comprehensive look at U.S. timberland investing. In particular, we consider the time series of historical returns to timberland, including the effect of industry maturity on returns, the impact of inflation on timberland returns, and expectations for future returns.

Private equity (PE) investments are investments in privately-held companies, that trade directly between investors instead of via organized exchanges. PE is often considered a distinct asset class, and it differs from investments in public equity in fundamental ways. There is no active market for PE positions, making these investments illiquid and difficult to value. PE funds typically have horizons of 10-13 years, during which the invested capital cannot be redeemed.

We survey the academic research concerning the risks and returns of institutional PE investments, as well as the optimal holdings of PE in an investment portfolio. Researchers have had limited access to information about the nature and performance of PE investments, and so research in this area is still preliminary and often inconclusive.

This article considers what underlying exposures drive portfolio returns in periods of extreme market stress and dislocation. Those investors who were shocked in the recent financial crisis by a perceived failure of diversification, and are now keen to avoid a repeat of that painful experience, need to identify and correct unintended risk gaps and biases in their portfolios.

One way to look at the problem, which is increasingly gaining currency, is to consider it from a risk contribution perspective: it can be shown that even very diverse multi-asset class portfolios are typically dominated by broad equity risk, which can account for more than 80% of total risk. Consequently, the ongoing debates about new approaches to asset allocation and tail risk hedging are essentially trying to address the same problem, i.e., how one can limit or reshape a typical institutional investor’s exposure to broad equity risk to make the portfolio more robust.

The Volatility Exchange™ (VolX®) plans to launch futures and options contracts based upon the realized volatility of U.S. equity indices. The futures version is named RealVol™ futures (VOL), which settle to the RealVol indices known generically as RVOL™. The concept is both similar and dissimilar to the popular VIX® index and products marketed by the CBOE®. The two versions are similar in the notion that both VolX and CBOE are trying to provide volatility products to the marketplace. They are dissimilar because the VIX index and consequently VIX futures are based on implied volatility (the relative cost of options) while the RVOL index and consequently VOLs are based on realized volatility (the actual, historical movement of the underlying index).

VOLs are exchange-tradable instruments that function similarly to a forward-starting over-the-counter volatility swap. They are expected to be launched on U.S. equity indices in 2013 and will come in two varieties: a 1-month calculation period of realized volatility (1VOL™) and a 3-month calculation period of realized volatility (3VOL™)i. The goal of this paper is to demonstrate how a VOL overlay can enhance the return and/or reduce the standard deviation of an equity portfolio. We chose the S&P 500 Total Return Index on the assumption that VolX will roll out products based upon this index.

A considerable amount of academic hedge fund research deals with issues of data dependency, time dependency, and analytical dependency on empirical results. Simple examples of the impact of issues of data dependency include the use of alternative databases and the use of revised data in contrast to actual historical data. Simple examples of time dependency include concentration on specific periods for which results are not representative across different financial or economic periods. Analytical dependency also impacts empirical analysis. Popular software often uses different algorithms in similar analytical processes or simply does not make a particular algorithm available (e.g., lack of robust estimators in Excel based analysis packages). Lastly, issues of data dependency, time dependency, and analytical program dependency are often co-dependent. For instance, the sole availability of monthly returns which may not represent the return process of more frequent investment intervals may prevent researchers from dissecting the time period of analysis into more information specific areas and limit the analytical approaches used for removing or analyzing certain informational impacts (e.g., extreme values). Since many researchers do not have the resources or time to evaluate different data, time, or alternative analytical algorithms, considerable research remains focused on the use of monthly data over long time frames of analysis based on the use of familiar forms of analytical analysis.