Commodity Futures

Do value strategy returns vary exploitably over time and across asset classes? In their October 2017 paper entitled “Value Timing: Risk and Return Across Asset Classes”, Fahiz Baba Yara, Martijn Boons and Andrea Tamoni examine the power of value spreads to predict returns for individual U.S. equities, global stock indexes, global government bonds, commodities and currencies. They measure value spreads as follows:

• For individual stocks, they each month sort stocks into tenths (deciles) on book-to-market ratio and form a portfolio that is long (short) the value-weighted decile with the highest (lowest) ratios.
• For global developed market equity indexes, they each month form a portfolio that is long (short) the equally weighted indexes with book-to-price ratio above (below) the median.
• For each other asset class, they each month form a portfolio that is long (short) the equally weighted assets with 5-year past returns below (above) the median.

To quantify benefits of timing value spreads, they test monthly time series (in only when undervalued) and rotation (weighted by valuation) strategies across asset classes. To measure sources of value spread variation, they decompose value spreads into asset class-specific and common components. Using monthly data for liquid U.S. stocks during January 1972 through December 2014, spot prices for 28 commodities during January 1972 through December 2014, spot and forward exchange rates for 10 currencies during February 1976 through December 2014, modeled and 1-month futures prices for ten 10-year government bonds during January 1991 through May 2009, and levels and book-to-price ratios for 13 developed equity market indexes during January 1994 through December 2014, they find that:

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Should retirement portfolios include an allocation to managed futures? In his October 2017 paper entitled “Using Trend-Following Managed Futures to Increase Expected Withdrawal Rates”, Andrew Miller compares seven 30-year retirement scenarios via backtests and modified backtests. Specifically, he compares maximum annual real withdrawal rates as a percentage of initial assets that do not exhaust any 30-year retirement portfolios starting each year during 1926-2012 (SAFEMAX). The seven scenarios, all rebalanced annually, are:

1. Historical Returns 50-50: uses actual annual returns for a 50% allocation to large-capitalization U.S. stocks and a 50% allocation to intermediate-term U.S. Treasuries.
2. Historical Returns 50-40-10: same as Scenario 1, except shifts 10% of the Treasuries allocation to a trend-following managed futures strategy that is long and short 67 stocks, bonds, currencies and commodities futures series based on equally weighted 1-month, 3-month and 12-month past returns with a 10% annual volatility target.
3. Lower Historical Returns 50-50: same as Scenario 1, but reduces monthly returns for stocks and Treasuries by 0.19%, reflecting end-of-2016 valuations.
4. Lower Historical Returns 50-40-10: same as Scenario 2, but reduces monthly returns for stocks, Treasuries and managed futures by 0.19%.
5. Lower Managed Futures Sharpe Ratio 50-40-10: same as Scenario 2, but reduces the Sharpe ratio for managed futures from an historical level to 0.5.
6. Lower Historical Returns/Lower Managed Futures Sharpe Ratio 50-40-10: same as Scenario 4, but reduces Sharpe ratio for managed futures to 0.5.
7. Historical Returns 50-50 with Trend Following for Stocks: same as Scenario 1, but each month puts the stocks allocation into stocks (30-day U.S. Treasury bills) when the return on stocks is positive (negative) over the prior 12 months.

He ignores all trading frictions, fees and taxes. Using monthly asset class returns as specified and monthly inflation data during January 1926 through December 2012, he finds that: Keep Reading

Does average sign of recent returns work as well as recent cumulative return as a momentum metric? In their May 2017 paper entitled “Returns Signal Momentum”, Fotis Papailias, Jiadong Liu and Dimitrios Thomakos introduce and test a momentum strategy (RSM) based on the equally weighted average signs (1 for positive and 0 for negative) of past returns over a given lookback interval. This metric employs each of the past returns during the lookback interval, not a single cumulative return as in times series (intrinsic or absolute) momentum. It considers only signs of past returns, not their magnitudes as in conventional relative momentum. They focus on monthly returns over a lookback interval of 12 months. They test RSM on a universe of 55 of the most liquid futures/forwards: 24 commodities; 9 currency exchange rates versus the U.S. dollar; 9 developed country equity indexes; and, 13 government bonds of various maturities from six developed countries. Their strategy is each month long (short) a contract series when average sign of its last 12 monthly returns is above (below) a threshold. They consider two types of thresholds: (1) fixed over the test period, with the featured optimal value selected by experimentation; and, (2) time-varying, each month choosing the best-performing value (from among 0.2, 0.3, 0.4, 0.5, 0.6, 0.7 and 0.8) over the prior 24 months. Using returns for the 55 futures/forwards series as available to support a strategy test period of January 1985 through March 2015, they find that:
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Does a carry trade derived from roll yields of futures/forward contracts work within asset classes (undiversified) and across asset classes (iversified)? In his May 2017 paper entitled “Optimising Cross-Asset Carry”, Nick Baltas explores the profitability of cross-sectional (relative) and time-series (absolute) carry strategies within and across futures/forward markets for currencies, stock indexes, commodities and government bonds. He posits that contracts in backwardation (contango) present a positive (negative) roll yield and should generally be overweighted (underweighted) in a carry portfolio. He considers three types of carry portfolios, each reformed monthly:

1. Cross-sectional (XS) or Relative – Rank all assets within a class by strength of carry, demean the rankings such that half are positive and half are negative and then assign weights proportional to demeaned ranks to create a balanced long-short portfolio. Combine asset classes by applying inverse volatility weights (based on 100-day rolling windows of returns) to each class portfolio.
2. Times-series (TS) or Absolute – Go long (short) each asset within a class that is in backwardation (contango), such that the class may be net long or short. Combine asset classes in the same way as XS.
3. Optimized (OPT) – Apply both relative strength and sign of carry to determine gross magnitude and direction (long or short) of positions for all assets, and further apply asset volatilities and correlations (based on 100-day rolling windows of returns) to optimize return/risk allocations.

Using daily data for 52 futures series (20 commodities, eight 10-year government bonds, nine currency exchange rates versus the U.S. dollar and 15 country stock indexes) during January 1990 through January 2016, he finds that: Keep Reading

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

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

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

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

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

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