Commodity Futures

How do the behaviors of time-series (absolute) and cross-sectional (relative) carry, momentum and value strategies differ? In the November 2015 version of their paper entitled “Dissecting Investment Strategies in the Cross Section and Time Series”, Jamil Baz, Nicolas Granger, Campbell Harvey, Nicolas Le Roux and Sandy Rattray explore time-series and cross-sectional carry, momentum and value strategies as applied to multiple asset classes. They adapt to each asset class the following general definitions:

• Carry – buy (sell) futures on assets for which the forward price is lower (higher) than the spot price.
• Momentum – buy (sell) assets that have outperformed (underperformed) over the past 6-12 months.
• Value – buy (sell) assets for which market price is lower (higher) than estimated fundamental price.

For cross-sectional portfolios, they rank assets within each class-strategy and form portfolios that are long (short) the equally weighted six assets with the highest (lowest) expected returns, rebalanced daily except for currency carry and value trades. For time-series portfolios, they take an equal long (short) position in each asset within a class-strategy according to whether its expected return is positive (negative). When combining strategies within an asset class, they use equal weighting. When combining across asset classes, they scale each class-strategy portfolio to a 15% annualized volatility target. Using daily contract closing bid-ask midpoints for 26 equity futures, 14 interest rate swaps, 31 currency exchange rates and 16 commodity futures during January 1990 through April 2015, they find that: Keep Reading

Does the third moment (skewness) of commodity futures return distributions predict subsequent returns? In the October 2015 version of their paper entitled “Commodities as Lotteries: Skewness and the Returns of Commodity Futures”, Adrian Fernandez-Perez, Bart Frijns, Ana-Maria Fuertes and Joelle Miffre examine the relationship between skewness and future returns in commodity futures markets. They calculate futures series returns as the difference in logarithmic settlement prices based on holding the nearest-to-maturity contract until one month to maturity and then rolling to the second nearest contract. They compute futures series skewness based on the last 12 months of daily returns. They study skewness effects by ranking futures into fifths (quintiles) based on past skewness. Using daily settlement prices for 27 commodity futures contract series (12 agriculture, 5 energy, 4 livestock, 5 metal and random length lumber) during January 1987 through November 2014, they find that: Keep Reading

Do behaviors of commodity futures over the past decade require updating of beliefs based on earlier research? In their August 2015 paper entitled “Conquering Misperceptions about Commodity Futures Investing”, Claude Erb and Campbell Harvey update and interpret research on returns from a passive, continuous investment in commodity futures. They focus on:

1. Relative contributions of price return and income components, with the latter comprised of roll return (switching from expiring to newer contracts) plus collateral return (cash deposits).
2. Whether or not commodity futures represent an asset class.
3. Whether passive commodity futures investment performance is comparable to that of passive investment in stocks.

Based on 1970 through 2004 research and an update spanning December 2004 through June 2015 focused on S&P GSCI returns, they find that: Keep Reading

Can traders use S&P 500 Implied Volatility Index (VIX) options to exploit predictability in behaviors of underlying VIX futures. In his June 2015 paper entitled “Trading the VIX Futures Roll and Volatility Premiums with VIX Options”, David Simon examines VIX option trading strategies that:

1. Buy VIX calls when VIX futures are in backwardation (difference between the front VIX futures and VIX, divided by the number of business days until expiration of the VIX futures, is greater than +0.1 VIX futures point).
2. Buy VIX puts when VIX futures are in contango (difference between the front VIX futures and VIX, divided by the number of business days until expiration of the VIX futures, is less than -0.1 VIX futures point).
3. Buy VIX puts when the VIX options-futures volatility premium (spread between VIX option implied volatility and lagged 10-trading day VIX futures volatility adjusted for number of trading days to expiration) is greater than 10%.

He measures trade returns for a holding period of five trading days, with entry and exit at bid-ask midpoints. An ancillary analysis relevant to strategy profitability looks at hedged returns on VIX options to determine whether they are overpriced: (1) generally; and, (2) for the top 25% of VIX options-futures volatility premiums. Using daily data for VIX options data and for VIX futures (nearest contract with at least 10 trading days to expiration) during January 2007 through March 2014, he finds that: Keep Reading

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