In a new whitepaper published by the Chartered Alternative Investment Analysts Association (CAIA Association), authors Daniel Chung and Xiaowei Kang of S&P Dow Jones Indices offer a full-scale examination of the use of alternative beta strategies in commodities investing. After pointing out that the correlation between traditional asset classes (stocks and bonds) increased steadily in the run-up to the 2008-09 financial crisis, Chung and Kang say the losses investors experienced inspired them to explore “alternative beta” strategies – i.e., strategies that attempt to mitigate risk or outperform the market without active management. These strategies, which are already popular with alternative equity investors, can be further subdivided into risk-based and factor-based varieties. Mr. Chung and Mr. Kang examine both approaches as applied to commodities.
Commodities Indices and Risk-Based Strategies
The two traditional commodities indices are the S&P GSCI and the Dow Jones UBS Commodity Index, which each suffer from using global production and trading liquidity as the primary factors in assigning weights to sectors and commodities. This leads to an overrepresentation of energy in both indices, and energy is the commodities sector that has historically exhibited the most volatility. For this reason, Chung and Kang suggest “a cautious passive approach may be to concentrate on reducing the risks of the commodity allocation,” by one of two means: risk-weight or minimum-variance.
The risk-weight strategy aims to allocate a similar risk budget to each of the five commodity sectors: energy, industrial metals, livestock, precious metals, and agricultural products. The minimum-variance strategy aims to minimize volatility of the portfolio as a whole. According to Chung and Kang’s research, the risk-weight strategy has resulted in superior returns, both in absolute and risk-adjusted terms, which is likely because “commodity prices and volatility go hand in hand,” so targeting the lowest possible volatility “appears counterintuitive.”
As you can see from the table above, the risk-weight portfolio produced superior returns to the minimum variance portfolio, based on data from December 31, 1999 to December 31, 2012. The minimum-variance portfolio had the lowest volatility, but that should be expected, as it’s the portfolio’s only objective! When also considering the S&P GSCI Light Energy index, you can see that the passive approach would have resulted in the best returns over this time frame – but also consider the maximum drawdown of 60.7%, compared to the much smaller maximum drawdown of the risk-weight portfolio; and the lower return per unit of risk for the S&P index compared to the risk-weight strategy.
A factor-based strategy aims to enhance returns by selecting investments using a rules-based approach according to predetermined factors. These factors can be just about anything, except they cannot be related to size, since that’s the primary factor of the standard indices with which factor-based strategies are attempting to reduce their correlation.
Chung and Kang examine the most well-known factors for commodities investing: value, curve, momentum, and liquidity.
- Value strategies select commodities with prices believed to be “out of kilter” with their basic supply-and-demand dynamics; i.e., they are undervalued and likely to appreciate;
- Curve strategies attempt to mitigate the negative effects of contago, which is the irregular situation in which futures prices are higher than spot prices; and they do this by receiving a premium for taking greater risk associated with longer-term futures contracts;
- Momentum strategies aim to ride out trends (“persistence in returns”) that are thought to be based on psychological biases “well-documented in the behavioral finance literature;” and
- Liquidity strategies make trades in consideration of the “rollover” effects created by the rebalancing of the S&P GSCI and the Dow Jones UBS Index.
Chung and Kang thoroughly examine all four factor-based approaches in the whitepaper, with charts and tables supplying data to support their assertions. Below is a chart demonstrating the long-term returns of each of the four strategies, ranging from December 1999 to November 2012. As noted by Chung and Kang, “correlation between these strategies is low” and “correlation between these strategies and the broad index is low to negative, with the exception of the momentum factor.”
The correlations for each strategy with the S&P GSCI Light Energy index were as follows:
- Value: -0.017
- Curve: -0.056
- Momentum: 0.428
- Liquidity: 0.25
Daniel Chung and Xiaowei Kang also examine the combination of different alternative-beta strategies to provide independent sources of risk premia in the commodity markets, and find that the individual strategies can serve as “building blocks” for well-diversified multi-asset portfolios. “From our investigation in this study,” they write, “there appears to be potential benefit in allocating into alternative beta strategies as part of a portfolio’s commodity allocation,” and furthermore, “we find that combining risk-based and factor-based commodity strategies has historically delivered higher return and lower risk than passive long-only strategies on their own.”
For more information, download a pdf copy of the whitepaper.