Commodity Futures

Commodity futures embed spot price expectations, and investors in futures seek a premium for bearing the risk that these expectations are wrong. Is the behavior of the risk premium for commodity futures over the last decade consistent with prior research? In their May 2015 paper entitled “Facts and Fantasies About Commodity Futures Ten Years Later”, Geetesh Bhardwaj, Gary Gorton and Geert Rouwenhorst update a study of commodity futures returns based on an equally‐weighted index of 36 contract series spanning energy, metals, grains and oilseeds, animal products and agricultural softs with ten years of additional data. They assume positions are fully collateralized (by equal positions in U.S. Treasury bills) and rebalanced monthly. They focus on differences between findings from the prior study and findings for the last ten years. Using monthly total returns for the specified equally-weighted commodity futures index, the S&P 500 index and a long-term U.S. Treasury bonds index during July 1959 through December 2014, they find that: Keep Reading

Does the interaction of paradigmatic indicators of optimism (lumber demand) and pessimism (gold demand) tell investors when to take risk and when to avoid risk? In their May 2015 paper entitled “Lumber: Worth Its Weight in Gold: Offense and Defense in Active Portfolio Management”, Charles Bilello and Michael Gayed examine the recent relative performance of lumber (a proxy for economic activity via construction) and gold (a safe haven) as an indicator of future stock market and bond market performance. Specifically, if lumber futures outperform (underperform) spot gold over the prior 13 weeks, they go on offense (defense) the next week. They test this strategy on combinations of seven indexes comprising a spectrum of risk (listed lowest to highest): BofA Merrill Lynch 5-7 Year Treasury Index (Treasuries); CBOE S&P 500 Buy-Write Index (BuyWrite); S&P 500 Low Volatility Index (Low Volatility); S&P 500 Index (SP500); Russell 2000 Index (R2000); Morgan Stanley Cyclicals Index (Cyclicals); and, S&P 500 High Beta Index (High Beta). Using weekly nearest futures contract prices for random length lumber, weekly spot gold prices and weekly total returns for the seven test indexes during November 1986 (November 1990 for Low Volatility and High Beta) through January 2015, they find that: Keep Reading

Does fourth quarter global economic data set the stage for asset class returns the next year? In their February 2015 paper entitled “The End-of-the-year Effect: Global Economic Growth and Expected Returns Around the World”, Stig Møller and Jesper Rangvid examine relationships between level of global economic growth and future asset class returns, focusing on growth at the end of the year. Their principle measure of global economic growth is the equally weighted average of quarterly OECD industrial production growth in 12 developed countries. They perform in-sample tests 30 countries and out-of-sample tests for these same 12 countries (for which more data are available). Out-of-sample tests: (1) generate initial parameters from 1970 through 1989 data for testing during 1990 through 2013 period; and, (2) insert a three-month delay between economic growth data and subsequent return calculations to account for publication lag. Using global industrial production growth as specified, annual total returns for 30 country, two regional and world stock indexes, currency spot and one-year forward exchange rates relative to the U.S. dollar, spot prices on 19 commodities, total annual returns for a global government bond index and a U.S. corporate bond index, and country inflation rates as available during 1970 through 2013, they find that: Keep Reading

Is the Commodity Trading Advisor (CTA) segment so crowded that flows of funds into or out of them around the turn of the month materially affect prices? In the October 2014 version of his paper entitled “The MOM-TOM Effect: Detecting the Market Impact of CTA Trading”, Otto Van Hemert explores whether the trend-following or time series momentum (MOM) style employed by many CTAs is so crowded that inflows around the turn of the month (TOM) affect momentum strategy returns. He notes that most CTA-managed funds offer monthly liquidity, thereby concentrating flows at month ends. He defines TOM as the last two days of a month plus the first day of the next month. He tests whether there is an above average return for MOM strategies during TOM (MOM-TOM effect). He uses the Newedge CTA Index (an equal-weighted aggregate of the largest CTAs open to new investments) and the Newedge Trend Index (an equal-weighted aggregate of the MOM style CTAs that are open to new investments) as proxies for the overall market and the MOM style, respectively. Using daily returns for these two indexes during January 2000 through March 2014, he finds that: Keep Reading

How do commodity futures behave in the post-financialization era, with commodities easily accessible via exchange-traded instruments and futures? In their September 2014 paper entitled “Factor Structure in Commodity Futures Return and Volatility”, Peter Christoffersen, Asger Lunde and Kasper Olesen analyze commodity return and volatility dynamics since financialization (after deregulation of commodity markets in the early 2000s). They consider 15 contract series comprised of the three most heavily traded of each of energy (light crude, natural gas, heating oil), metals (gold, silver, copper), grains (soybeans, corn, wheat), softs (sugar, coffee, cotton) and meats (live cattle, lean hogs, feeder cattle). They focus on: whether factors might explain commodity returns and volatilities, and integration of commodity markets with the equity market. In assessing continuous positions, they roll from an expiring commodity contract to the subsequent contract when daily volume of the latter exceeds that of the former. Using daily returns derived from over 750 million commodity futures contract trades for the selected 15 series and for SPDR S&P 500 (SPY) during January  2004 through December 2013, they find that: Keep Reading

Is there a simple way to tell whether a commodity is overvalued or undervalued? In his May 2014 presentation package entitled “Commodity ‘CAPE Ratios'”, Claude Erb looks at long-term real commodity prices as valuation “crutches” to estimate when commodities are overvalued and undervalued. He provides examples relating real commodity prices to future long-term (10-year) real commodity returns. He employs the U.S. consumer price index (CPI) for inflation adjustment. Using gold price since January 1975, the S&P GSCI Index since January 1970, corn price since April 1965, crude oil price since March 1983 and contemporaneous CPI data through April 2014, he finds that: Keep Reading

Should investors seeking leveraged positions in gold prefer futures or leveraged exchanged-traded funds (ETF)? In their August 2014 paper entitled “Price Dynamics of Gold Futures and Gold Leveraged ETFs”, Tim Leung and Brian Ward compare the price evolutions of spot gold, gold futures and leveraged gold ETFs. They use the XAU-USD gold-U.S. dollar exchange rate as the spot gold price. Among gold futures, they consider maturities from nearest month to one year. Among ETFs, they consider the unleveraged iShares GLD, the ProShares 2X UGL, the ProShares -2X GLL, the VelocityShares 3X UGLD and the VelocityShares -3X DGLD. They also construct static and dynamic portfolios of gold futures in efforts to replicate spot gold and leveraged gold price behaviors. Using recent gold futures and gold ETF prices through 7/14/2014, they find that:

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Does focus on nearest-expiration contracts in commodity futures momentum strategies leave money on the table? In their May 2014 paper entitled “Exploiting Commodity Momentum Along the Futures Curves”, Wilma De Groot, Dennis Karstanje and Weili Zhou investigate commodities futures momentum strategies that consider all available contract expirations. They hypothesize that a broadened contract universe could increase roll yield, reduce volatility and lower portfolio turnover. Their generic benchmark strategy each month buys (sells) the equally weighted half of commodities with the highest (lowest) 12-month returns using nearest-expiration contracts. They consider three alternatives to the generic strategy:

1. Optimal-roll momentum: each month ranks commodities in the same way as the generic strategy, but buys the most backwardated contract for each winner commodity and sells the most contangoed contract for each loser commodity from among contracts with expirations up to 12 months.
2. All-contracts momentum: each month first select for each commodity the contract expiration with the strongest and weakest momentum. Then rank the commodities based on these contracts and buy (sell) the equally weighted half with the highest (lowest) momentum.
3. Low-turnover roll momentum: modify the optimal-roll momentum strategy by holding each position until it is about to expire or until it switches sides (long-to-short or short-to-long), whichever occurs first.

They assume fully collateralized portfolios, such that total monthly return for each position is change in month-end settlement price plus the risk-free interest rate (U.S. Treasury bill yield) earned by the collateral. They focus on changes in settlement prices (excess returns). They consider several ways of estimating trading frictions. Using daily and monthly prices of S&P GSCI components during January 1990 through September 2011 (initially 18 commodity series growing to all 24 by July 1997), they find that: Keep Reading

Has the growing role of financial investors in commodities markets (financialization) weakened performance of widely used momentum and term structure investing strategies? In his July 2014 paper entitled “Strategies Based on Momentum and Term Structure in Financialized Commodity Markets”, Adam Zaremba investigates impacts of financialization of commodity markets on the profitability of momentum and term structure strategies. His base momentum strategy is each month long (short) the half of commodity futures with higher (lower) returns over the past month. His base term structure strategy is long (short) the half of commodity futures with the largest positive or backwardated (negative or contangoed) difference in prices between the nearest and next-nearest contracts. For each commodity futures series and each strategy, he performs double-sorts on strategy parameters and the level of financial investor (non-commercial trader) participation from Commitments of Traders (COT) reports to measure the effects of financialization on strategy performance. All portfolios are equally weighted and fully collateralized. Using monthly total returns for 26 commodity futures series as available and a broad commodities index, along with position data from COT reports, during 1986 through 2013, he finds that: Keep Reading

What is the best way to generate price trend signals for trading futures/forward contracts? In their December 2013 paper entitled “CTAs – Which Trend is Your Friend?”, Fabian Dori, Manuel Krieger, Urs Schubiger and Daniel Torgler compare risk-adjusted performances of three ways of translating trends into trading signals:

1. Binary signals (up or down) trigger 100% long or 100% short trades. When trends are strong (ambiguous), this approach generates little trading (whipsaws/over-commitment to weak trends). The price impact of trading via this approach may be substantial for large traders.
2. Continuously scaled signals trigger long or short trades with position size scaled according to the strength of up or down trend; the stronger the trend, the larger the position. Changes in trend strength generate incremental position adjustments.
3. Empirical distribution signals trigger long or short trades with position size scaled according to the historical relationship between trend strength and future return. The strongest trend may not indicate the strongest future return, and may actually indicate return (and therefore position) reversal. Changes in trend strength generate position adjustments.

They test these three approaches for comparable trends exhibited by 96 futures/forward contract series, including: 30 currency pairs, 19 equity indexes, 11 government bond indexes, 8 short-term interest rates (STIR) and 28 commodities. They consider two risk-adjusted return metrics: annualized return divided by annualized volatility, and annualized return divided by maximum drawdown. They ignore trading frictions. Using prices for these 96 series from 1993 to 2013, they find that: Keep Reading