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Commodity Futures

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

(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

Commodity Futures Trading Principles

How should investors approach commodity futures trading? In her August 2016 paper entitled “An Introduction to U.S. Commodity Futures Markets: a Historical Perspective Along with Commodity Trading Principles”, Hilary Till summarizes success factors for designing and managing a commodity futures portfolio, including: identifying potential trades; constructing trades; constructing a portfolio of trades; risk management; payoff expectations; level of leverage; and, interaction of the commodity futures portfolio with holdings of other asset classes. Based on her experience and empirical examples, she concludes that: Keep Reading

How Best to Invest in Oil?

How should investors think about investing in crude oil? In their June 2016 paper entitled “Understanding Oil Investing”, Ludwig Chincarini, John Love and Robert Nguyen examine oil investing, with emphasis on differences in behaviors between non-investable spot oil and investable crude oil futures. They consider several approaches to futures, all fully collateralized by cash (one-month U.S. Treasury bills):

  1. Simple systematic rolling – Select a contract series (nearest, 2nd, 3rd… from expiration) and systematically roll from one contract in the series to the next at a specified time before expiration (0, 1, 3, 5, 10, 13…  days).
  2. Binary signaled rolling (Strategy 1) – Acquire/roll to the next contract in a series only when the series is in backwardation (has positive roll yield) and otherwise hold cash.
  3. Highest roll yield (Strategy 2) – Acquire/roll to the one of the nearest, 2nd or 3rd contract with the highest backwardation (or lowest contango) on a specified roll date.

They also examine the behaviors of crude oil exchange-traded funds (ETF). Using daily spot West Texas Intermediate (WTI) crude oil price and WTI crude oil futures prices, volumes and open interest during 1983 through 2015 (focusing on 1994-2015 and 2005-2015), and crude oil ETF prices from inceptions through early 2016, they find that: Keep Reading

Feasibility of Cloning CTA-like Funds

Should investors believe that the financial industry can offer low-cost, liquid funds that reliably mimic Commodity Trading Advisor (CTA) hedge funds? In their June 2016 paper entitled “Just a One Trick Pony? An Analysis of CTA Risk and Return”, Jason Foran, Mark Hutchinson, David McCarthy and John O’Brien identify and examine performances of CTA-like hedge funds across eight distinct categories defined via iterative correlation clustering. Their goal is to determine whether category performance is amenable to modeling (cloning) via liquid exposures to four futures risk factor premiums:

  1. Value – long (short) high-value (low-value) futures, with “value” based on book-to-market ratios for stock index futures and 5-year change in yields/spot prices/purchasing power for government bonds/commodities/currency forwards.
  2. Carry – long (short) futures with high (low) roll returns.
  3. Time series momentum – long (short) futures with positive (negative) 12-month past returns.
  4. Options-based trend following – from Fung and Hsieh, correlated with trends shorter than time series momentum.

They estimate these premiums from monthly returns of rolling nearest contracts for: 12 global equity index futures series; eight global 10-year government bond synthetic futures series; 22 commodity futures series; and, nine global currency forward series versus the U.S. dollar. They employ a hedge fund screening process that suppresses backfill bias (lucky starts). Using monthly net returns and assets under management (AUM) for specific (not fund-of-funds) and distinct CTA funds with at least 12 months of returns denominated in U.S. dollars and monthly data required to estimate futures risk factor premiums as available during January 1987 through July 2015, they find that: Keep Reading

Benchmarking Trend-following Managed Futures

Is there an objective way to benchmark the performance of trend-following Managed Futures hedge funds? In their March 2016 paper entitled “Adaptive Time Series Momentum – Benchmark for Trend-Following Funds”, Peter Erdos and Gert Elaut test a futures timing system that increases (decreases) allocations when trends are emerging (fading) per 251 equally weighted, volatility-scaled, daily rebalanced time series momentum (TSMOM) strategies. Strategy lookback intervals range from 10 to 260 trading days. Volatility scaling involves dividing momentum returns by an exponentially weighted daily moving average estimator of volatility over a 60-day rolling window. They account for trading frictions (bid-ask spread plus broker/market fees by asset class, estimated separately for old and new subperiods), exchange rates, one-day signal-to-trade execution delay and estimated management/performance fees. They apply the TSMOM system as a mechanical benchmark for trend-following Managed Futures hedge funds. They examine also a momentum “speed factor” that buys longer-term and sells shorter-term TSMOM strategies. Using daily prices for 98 futures contract series and monthly net-of-fee returns for 379 live and dead trend-following Managed Futures hedge funds during January 1994 through September 2015, they find that: Keep Reading

Commodity-Currency Interactions

Do commodity price changes predict currency exchange rate fluctuations for commodity-exporting countries? In their March 2016 paper entitled “When the Walk is Not Random: Commodity Prices and Exchange Rates”, Emanuel Kohlscheen, Fernando Avalos  and Andreas Schrimpf analyze relationships between commodity prices and exporter exchange rates. They first construct daily commodity export price indexes tailored to 11 commodity-exporting countries (Australia, Brazil, Canada, Chile, Colombia, Malaysia, Mexico, Norway, Peru, Russia, South Africa), encompassing 83 commodities (26 metal, 36 agricultural, 11 livestock, 10 energy). They then relate index levels to daily currency exchange rates by country. Using daily UN Comtrade statistics, commodity prices and currency exchange rates in U.S. dollars and Japanese yen as available during January 2004 (Malaysia starts in August 2005, and Russia starts in February 2009) through February 2015, they find that: Keep Reading

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