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Commodity Futures

These entries address investing and trading in commodities and commodity futures as an alternative asset class to equities.

Common Commodity Futures Trading Strategies

What are the most common strategies for trading commodity futures? In their brief January 2017 article entitled “Commodity Futures Trading Strategies: Trend-Following and Calendar Spreads”, Hilary Till and Joseph Eagleeye describe the two most common strategies among commodity futures traders: (1) trend-following, wherein non-discretionary traders automatically screen markets based on technical factors to detect beginnings and ends of trends across different timeframes; and, (2) calendar-spread trading, wherein traders exploit commercial/institutional supply and demand mismatches that affect price spreads between commodity futures contract delivery months. Examples of the latter are seasonal inventory build and draw cycles (as for natural gas) and precise roll cycles for expiring contracts included in commodity futures indexes. Based on the body of research and examples, they conclude that: Keep Reading

Implied Volatility Trading Strategy for Commodity Futures

Is option-implied volatility a useful predictor of returns for commodity futures? In her March 2017 paper entitled “Commodity Option Implied Volatilities and the Expected Futures Returns”, Lin Gao tests the power of option-implied volatilities (with 12-month detrending) for commodities to predict commodity futures returns. Specifically, she each month buys (sells) the fourth of commodities with the lowest (highest) detrended implied volatilities at of the end of the preceding month. To generate continuous return series for liquid commodity futures contracts, she rolls contracts when time-to-expiration decreases to one month. She further compares the implied volatility hedge strategy to five other commodity futures hedge strategies (specified below): (1) momentum; (2) basis; (3) basis-momentum; (4) hedging pressure; and, (5) growth in open interest expressed indollars. Using options data for 25 commodities to calculate end-of-month implied volatilities and contemporaneous commodity futures price and open interest data as available during January 1990 through October 2014, she finds that: Keep Reading

Commodity Futures Return Predictability

Are aggregate commodity futures returns predictable based on prices across the maturity curve and/or on the state of the global economy? In her January 2017 paper entitled “Commodity Return Predictability”, Regina Hammerschmid investigates aggregate commodity futures return predictability based on variables incorporating information from the term structure of futures prices and several global economic variables. She includes commodity futures series spanning five sectors (energy, grains/oilseeds, livestock, metals and softs). She considers three groups of predictive variables: (1) commodities spot and futures prices; (2) aggregate OECD economic data (industrial production, total exports and imports, the composite leading indicator and business confidence index); and, (3) for comparison tests, commodities trading volume, open interest and hedging pressure (net difference between short and long positions of hedgers). She uses returns for fully collateralized long positions in commodity futures contracts with 1, 2, 3 and 4 months to maturity, rolled at the end of each month. She aggregates returns by first averaging within each sector and then averaging sector averages (all equally weighted). She considers forecast horizons of 1, 3, 6, 9 or 12 months. For out-of-sample regression testing, she uses an inception-to-date window of at least 10 years of data. Using daily spot and commodity futures settlement prices as available, monthly economic data and monthly S&P-GSCI levels since January 1975, and associated monthly trading volume, open interest and hedging pressure data as available since January 1986, all through August 2015, she finds that: Keep Reading

Trading Price Jumps

Is there an exploitable short-term momentum effect after asset price jumps? In his January 2017 paper entitled “Profitability of Trading in the Direction of Asset Price Jumps – Analysis of Multiple Assets and Frequencies”, Milan Ficura tests the profitability of trading based on continuation of jumps up or down in the price series of each of four currency exchange rates (EUR/USD, GBP/USD, USD/CHF and USD/JPY) and three futures (Light Crude Oil, E-Mini S&P 500 and VIX futures). For each series, he looks for jumps in prices measured at seven intervals (1-minute, 5-minute, 15-minute, 30-minute, 1-hour, 4-hour and 1-day). His statistical specification for jumps uses returns normalized by local historical volatility. He separately tests the last 4, 8, 16, 32, 64, 128 or 256 measurement intervals for the local volatility calculation, and he considers jump identification confidence levels of 90%, 95%, 99% or 99.9%. His trading system enters a trade in the direction of a price jump at the end of the interval in which the jump occurs and holds for a fixed number of intervals (1, 2, 4, 8 or 16). He thus considers a total of 6,860 strategy variations across asset price series. He divides each price series into halves, employing the first half to optimize number of volatility calculation measurement intervals, confidence level and number of holding intervals for each measurement frequency. He then tests the optimal parameters in the second half. He assumes trading frictions of one pip for currencies, and one tick plus broker commission for futures. He focuses on drawdown ratio (average annual profit divided by maximum drawdown) as the key performance metric. He excludes price gaps over weekends and for rolling futures contracts. Using currency exchange rate data during November 1999 through mid-June 2015, Light Crude Oil futures data during January 1987 through early December 2015, E-Mini S&P 500 futures during mid-September 1999 through early December 2015 and VIX futures during late March 2004 through early December 2015, he finds that: Keep Reading

Oil Futures Term Structure and Future Stock Market Returns

Does the term structure of crude oil futures predict stock market returns? In their October 2016 paper entitled “Do Oil Futures Prices Predict Stock Returns?”, I-Hsuan Chiang and Keener Hughen examine the ability of crude oil futures prices to predict U.S. stock market returns. They identify the first three principal components of the nearest six oil futures prices. After finding that one of these components (related to the term structure) predicts stock market returns, they define a simple oil futures term structure curvature factor as:

  • Short-term slope (natural logarithm of the second nearest price minus natural logarithm of the nearest price), minus
  • Long-term slope (natural logarithm of the sixth nearest price minus natural logarithm of the third nearest price).

They test the ability of this curvature factor to predict U.S. stock market performance and industry performance in-sample (based on returns) and out-of-sample (based on R-squared explanatory power) at a one-month horizon. They compare its out-of-sample predictive power with those of nine other widely used predictors: dividend-price ratio, dividend yield, earnings-price ratio, book-to-market ratio, long-term U.S. Treasuries yield, long-term U.S. Treasuries return, U.S. Treasuries yield spread, U.S. Treasury bills yield and default yield spread. Using daily prices for the six nearest WTI light crude oil futures contracts and monthly returns for the broad U.S. stock market, 49 value-weighted industries and stocks in four crude oil subsectors during March 1983 through December 2014, they find that: Keep Reading

(Some) Commodities over the Long Run

Are commodity futures an attractive asset class over the long run? In their October 2016 paper entitled “Commodities for the Long Run”, Ari Levine, Yao Hua Ooi and Matthew Richardson analyze commodity futures prices extending as far back as 1877. Their perspective is that futures price reflects both foregone interest and cost of storage for holding a commodity, with these costs potentially offset by a convenience yield associated with potential shortages of the commodity. They therefore decompose futures return into spot return in excess of cash (spot return minus short-term interest rate) and net convenience yield (roll yield plus short-term interest rate) rather than simple spot return and simple roll yield. The excess spot return represents a spot commodity risk premium, while the net convenience yield represents compensation for bearing inventory risk and/or providing liquidity to hedgers. They also report simple spot return and simple roll yield for comparison. They construct commodity futures indexes by, in general, each month holding the nearest contracts with delivery at least two months away. They examine commodity futures return behaviors during backwardation versus contango, and across business and inflation cycles. They compare commodity futures return behaviors to those of excess total returns on the aggregate U.S. stock market and long-term U.S. government bonds, and extend this analysis to consider the impacts of commodity futures on a diversified portfolio. Using monthly prices for 35 commodity futures (18 agricultural, 6 energy, 11 metals) as available, along with data for U.S. government bonds, the U.S. stock market and the U.S. economy, during 1877 through 2015, they find that: Keep Reading

Risk Aspects of Long and Short Futures Trend-following

How do the long and short sides of futures trend-following strategies differently affect portfolio riskiness? In their September 2016 paper entitled “The Long and Short of Trend Followers”, Jarkko Peltomaki, Joakim Agerback and Tor Gudmundsen-Sinclair investigate via linear regression behaviors of the long and short sides of commonly used trend-following strategies across equities, bonds, commodities and currency futures/forwards under different economic conditions. They model trend-following performance by combining two sets of rules: (1) four slow-reacting simple moving average pair crossover rules using 75-225, 100-300, 125-375 or 150-450 daily moving average pairs; and, (2) four fast-reacting moving average breakout rules based on fluctuations around a long-term moving average. They apply the same allocation method for all rules to set a constant initial risk per trade, adjusted daily by scaling inversely with volatility. They examine how long and short trend-following returns depend on economic environment, focusing on interest rates. They assume trading frictions total $30 per contract. Using futures contract data for 22 equity indexes, 15 government bonds, 17 commodities and six currencies relative to the U.S. dollar, and contemporaneous Commodity Trading Advisor (CTA) performance indexes, during 1984 through 2015, they find that: Keep Reading

Momentum in Commodity Futures and Reversion in Spot

Do spot price trends drive commodity futures momentum strategies? In their August 2016 paper entitled “Momentum and Mean-Reversion in Commodity Spot and Futures Markets”, Denis Chaves and Vivek Viswanathan investigate the reasons for the success of cross-sectional (relative) momentum strategies and failure of cross-sectional mean reversion strategies in the commodity futures markets. They specify commodity valuation as the ratio of current price to average price ratio over the past 120 months (P/A). They specify commodity price trend as cumulative return over measurement intervals ranging from the last month to the last 66 months. Using two independent sets of 25 (with liquid futures) and 21 (without liquid futures) commodity spot price series as available since 1946 and one set of 27 commodity futures price series as available since 1965, all through 2014, they find that: Keep Reading

Performance of Technical Trading Rules for Crude Oil Futures

Does technical analysis work for crude oil futures trading? In their August 2016 paper entitled “Performance of Technical Trading Rules: Evidence from the Crude Oil Market”, Ioannis Psaradellis, Jason Laws, Athanasios Pantelous and Georgios Sermpinis investigate the profitability of a wide range technical trading rules applied to West Texas Intermediate (WTI) light sweet crude oil futures and the United States Oil (USO) fund, which holds front-month futures contracts. They consider 7,846 trading rules grouped into five families (filter rules, moving averages, support and resistance rules, channel breakouts and on-balance volume averages) used on daily prices. They assume these rules earn the risk-free rate when neutral. They measure performance during four subperiods (April 2007-May 2009; June 2009-March 2011; April 2011-July 2013; August 2013-December 2015), reflecting bullish or bearish and contango or backwardation subperiods. They focus on average return, Sharpe ratio and Calmar ratio as key performance statistics. They begin with in-sample tests and progressively account for trading frictions (one-way 0.033% for futures and 0.05% for USO), data snooping bias (via two methods) and out-of-sample rule identification. Using daily prices for the specified assets during April 2006 through December 2015, they find that: Keep Reading

Long-term Tests of Intrinsic Momentum Across Asset Classes

Does time series (intrinsic or absolute) momentum work across asset classes prior to the Great Moderation (secular decline in interest rates)? In their August 2016 paper entitled “Trend Following: Equity and Bond Crisis Alpha”, Carl Hamill, Sandy Rattray and Otto Van Hemert test several time series momentum portfolios as applied to groups of bonds, commodities, currencies and equity indexes as far back as 1960. They consider 10 developed country equity indexes, 11 developed country government bond series, 25 agricultural/energy/metal futures series and nine U.S. dollar currency exchange rate series. They calculate return momentum for each asset as the weighted sum of its past monthly returns (up to 11 months) divided by the normalized standard deviation of those monthly returns. They then divide each signal again by volatility and apply a gearing factor to specify a 10% annual volatility target for each holding. Within each of equity index, bond and currency groups, they weight components equally. Within commodities, they weight agriculture, energy and metal sectors equally after weighting individual commodities equally within each sector. They report strategy performance based on excess return, roughly equal to real (inflation-adjusted) return. They commence strategy performance analyses in 1960 to include an extreme bond bear market. Using monthly price series that dovetail futures/forwards from inception with preceding spot (cash) data as available starting as early as January 1950 and as late as April 1990, all through 2015, they find that: Keep Reading

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