Benchmarking commodity investments

• Published date: 01 March 2018
• DOI: http://doi.org/10.1002/fut.21885
• Author: Jesse Blocher,Marat Molyboga,Ricky Cooper
• Date: 01 March 2018

Received: 16 June 2017

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Accepted: 10 September 2017

DOI: 10.1002/fut.21885

RESEARCH ARTICLE

Benchmarking commodity investments

Jesse Blocher

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Ricky Cooper

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Marat Molyboga

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Owen Graduate School of Management,

Vanderbilt University, Nashville, Tennessee

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Stuart School of Business, Illinois Institute

of Technology, Chicago, Illinois

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Stuart School of Business, Illinois Institute

of Technology, Warrenville, Illinois

Correspondence

Marat Molyboga, Efficient Capital

Management and Adjunct Faculty at the

Stuart School of Business, Illinois Institute

of Technology, Warrenville, Illinois.

Email: molyboga@efficientcapital.com

Funding information

Vanderbilt's Financial Markets Research

Center; Chicago Mercantile Exchange

While much is known about the financialization of commodities, less is known about

how to profitably invest in commodities. We develop a four-factor asset pricing

model of commodity returns. Our four-factor model prices both commodity spot and

term risk premia in an intuitive manner related to investable portfolios. The

straightforward construction of our factors is an improvement over previous models.

Furthermore, our four-factor model prices commodity risk premia using both sorted

portfolios and risk adjusted alphas as benchmarks. Thus, we feel it is an appropriate

benchmark to evaluate commodity investment vehicles.

KEYWORDS

benchmarking, commodities, spot premia, term premia

Like most products in the liquid alternatives space, this is not a simple plug-and-play category, where any

above-average fund will suffice ...The challenge, as always, is finding the right manager. But it doesn't help

that the managed futures space is still a very long way from enabling simple and straightforward

comparisons. −Investment News, Jan 14, 2015, “Managed futures funds shine anew, but mystery remains”

Commodities “couldn’t be hated mor e”...Four years of negative retur ns for indices tracking future s, with a

fifth under way, have underm ined the idea that leaving part o f one's portfolio in a basket of oil, natural gas,

soyabeans, copper and oth er commodities was prudent. “Th ere's zero interest right no w from the institutional

space,”says Lawrence Loughl in of Drobny Capital. −Financ ial Times, June 3, 2015, “Investme nt: Revaluing

Commodities”

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INTRODUCTION

The literature on commodities dates back at least to Keynes (1923), but most of it focuses on production and storage decisions or

the role of commodities in international trade (Rouwenhorst & Tang, 2012). There is a large and growing literature around the

financialization of commodities, the purported cause of which is increasing investment by finance professionals or so-called

“speculators”(e.g., Cheng & Xiong, 2014). However, there has been less research about how astute investors should incorporate

commodities into a diversified portfolio. Since the global capital (institutional and retail) allocated to commodities is

approximately $330B, this is an important question.

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$330B comes from investment report from Barclays Capital Commodities Research via a HewittEnnisKnupp Global Invested Capital Report, June 2014.

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© 2017 Wiley Periodicals, Inc. wileyonlinelibrary.com/journal/fut J Futures Markets. 2018;38:340–358.

Indexing of commodity futures, especially equally weighted indexing, is an easily-implemented passive strategy. But this

approach has yielded negative or zero returns over much of its history, and practitioners are abandoning it.

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Figure 1 shows the

poor performance of an equally weighted market index since 1987. Gorton and Rouwenhorst (2006) and Erb and Harvey (2006)

have a robust discussion of the ambiguous desirability of index strategies.

Some papers have examined Commodity Trading Advisors (CTAs) (e.g., Bhardwaj, Gorton, & Rouwenhorst, 2014; Fung &

Hsieh, 1997, 2000). These papers have employed diversified (i.e., including non-commodity factors) factor models because only

19% of CTAs invest exclusively in commodities, despite their name (see Table 1). Fittingly, these studies have typically been

interpreted as research on hedge funds, rather than commodities (e.g., Bollen & Whaley, 2009; Kosowski, Naik, & Teo, 2007).

Narrowing the portfolio decision to this 19% of CTAs that invest solely in commodities (or “commodity funds”) may be the best

way to incorporate commodities into a diverse portfolio, but no commodity-specific benchmark exists with which to evaluate

these managers. Since Roll (1978) showed that different benchmarks can yield different rankings of “skill,”independently

identifying the right benchmark is a necessary first step to manager selection.

This paper establishes a parsimonious, tradeable,

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four-factor model benchmark, with which investors can evaluate

commodity fund managers (or other commodity investments, such as Exchange Traded Funds, or ETFs). Our model not only

prices commodity spot risk premia, but also commodity term risk premia, identified by Szymanowska, Roon, Nijman, and ven

den Goorbergh (2014). Our four-factor model fails to price only two test assets among five different four-way portfolio sorts (two

spot premia, three term premia, for a total of 20 portfolios).

The four factors in our model include a market factor, a time series momentum factor, and separate high and low term premia

factors, sorted on commodity basis. These factors are drawn from the extant literature and based in commodity fundamentals,

and each has been shown separately to capture a risk premium embedded in commodity futures, though never together in the

form we propose. While our focus is on benchmarking active managers, our factors can just as easily be thought of as composing

a single or multi-factor “smart beta”commodity ETF since they are tradeable and rules-based by construction.

To establish the power of our four-factor model, we run a horserace between our model and two popular models established

in the literature. The first is the popular model of Fung and Hsieh (2001), which we call the FH model. This model covers a wide

variety of strategies and is intended as a descriptive model to identify the strategies used by hedge funds and CTAs. One

drawback of this model is that the factors are not tradeable, making interpretation difficult. This model has also been criticized by

Bhardwaj et al. (2014), who argue that the negative performance of the factors means alpha identified based on this model is

spurious.

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The second model comes from Bhardwaj et al. (2014), who include factors for commodities, interest rate derivatives,

and currency futures. Since our focus is on commodities, we only test the model's commodity factors, which we call the BGR

model.

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We find that both our model and the BGR model price spot risk premia adequately. Both estimate an alpha equal to zero for

all test assets, have high adjusted R

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, and fail to reject the GRS test that all portfolio alphas are jointly set to zero (Gibbons, Ross,

& Shanken, 1989). The FH model fails to price several of the spot premia test portfolios and the GRS test rejects null hypothesis

of joint zero alpha for all portfolio sorts. The adjusted R

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for the FH model is zero for all test portfolios.

Our four-factor model is the only model that can consistently price term premia. The BGR and FH models can only

price 3 of 12 well (and BGR is borderline on a fourth). In contrast, our four-factor model, which includes two term premia

factors, successfully prices 10 of 12 test asset portfolios. At both the 4 and 6 month horizon, a GRS test of our four-factor

model fails to reject the null of zero alpha for all portfolios. At a 2 month horizon, our four-factor model prices 3 of 4

portfolios.

Until now, benchmarking commodity investments has been inhibited by a disagreement in the literature of the drivers of risk

premia. Recently, however, the literature has coalesced around a few key drivers, represented by the four factors in our model.

Simultaneously, increased interest in commodity investment in the past decade combined with the poor performance of passive

market indexes means sophisticated investors are more interested in evaluating the performance of active commodity fund

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Investment: Revaluing Commodities, June 3, 2015, Financial Times. http://www.ft.com/cms/s/0/a6ff2818-094c-11e5-8534-00144feabdc0.html, also the

source for the second opening quote. Also see Bhardwaj, Gorton, and Rouwenhorst (2015).

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We use tradeable to mean that the implementation of our factors as actual strategies is intuitively straight-forward. However, actual implementation on a

given set of commodities is subject to open interest considerations, and transaction costs.

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In some factor models, negative factor loadings could attribute positive performance to poorly performing factors. But since the Fung–Hseih factors are

not tradeable, this interpretation is not applicable.

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Bhardwaj et al. (2014) do not test their model, but simply assert it as capturing known trading patterns in commodities. The commodity-only version of

their model is also almost identical to a model proposed and tested more thoroughly in a working paper by Bakshi et al. (2014), thus we can refer to it as

the “BGR”model and use that to refer to both papers.

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