Commodity Futures

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

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Skewness as Commodity Futures Return Predictor

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

Updated Perspectives on Commodity Futures Investing

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

Exploiting VIX Futures Predictability with VIX Options

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

Exploiting VIX Futures Roll Return with ETNs

“Identifying VXX/XIV Tendencies” finds that S&P 500 implied volatility index (VIX) futures roll return, as measured by the percentage difference in settlement price between the nearest and next nearest VIX futures, may be a useful predictor of iPath S&P 500 VIX Short-term Futures ETN (VXX) and VelocityShares Daily Inverse VIX Short-term ETN (XIV) returns. Is there a way to exploit this predictive power? To investigate, we compare performance data for:

  1. Buying and holding XIV.
  2. Timing XIV to avoid times when the roll return is positive.
  3. Timing XIV and VXX to exploit both negative and positive roll return conditions.

Using daily closing prices for XIV and VXX and daily settlement prices for VIX futures from XIV inception (end of November 2010) through most of June 2015, we find that: Keep Reading

Identifying VXX/XIV Tendencies

A subscriber inquired about strategies for trading exchange-traded notes (ETN) constructed from near-term S&P 500 Volatility Index (VIX) futures: iPath S&P 500 VIX Short-Term Futures ETN (VXX) and VelocityShares Daily Inverse VIX Short-Term (XIV), available since 1/30/09 and 11/30/10, respectively. The managers of these securities buy and sell VIX futures daily to maintain a constant maturity of one month (long for VXX and short for XIV), continually rolling partial positions from the nearest term contract to the next nearest. We consider four potential predictors of the price behavior of these securities:

  1. The level of VIX, in case a high (low) level indicates a future decrease (increase) in VIX that might affect VXX and XIV.
  2. The change in VIX, in case there is some predictable reversion or momentum for VIX that might affect VXX and XIV.
  3. The term structure of VIX futures (roll return) underlying VXX and XIV, as measured by the percentage difference in settlement price between the nearest and next nearest VIX futures, indicating a price headwind or tailwind for a fund manager continually rolling from one to the other. Roll return is usually negative (contango), but occasionally positive (backwardation).
  4. The Volatility Risk Premium (VRP), estimated as the difference between VIX and the annualized standard deviation of daily S&P 500 Index returns over the past 21 trading days (multiplying by the square root of 250 to annualize), in case this difference between expectations and recent experience indicates the direction of future change in VIX.

We identify predictive power by relating daily VXX and XIV returns over the next 21 trading days to daily values of each indicator. Using daily levels of VIX, settlement prices for VIX futures contracts, levels of the S&P 500 Index and split-adjusted prices for VXX and XIV from inceptions of the ETNs through most of June 2015, we find that: Keep Reading

Updated Empirical Overview of 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

Lumber-Gold Interaction as Stocks and Bonds Indicator

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

Year-end Global Growth and Future Asset Class Returns

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

Interplay of the Dollar, Gold and Oil

What is the interplay among investable proxies for the U.S. dollar, gold and crude oil? Do changes in the value of the dollar lead those in hard assets? To investigate, we relate the return series of three exchange-traded funds: (1) the futures-based PowerShares DB US Dollar Index Bullish (UUP); (2) the spot-based SPDR Gold Shares (GLD); and, (3) the spot-based United States Oil (USO). Using monthly, weekly and daily prices for these funds during March 2007 (limited by inception of UUP) through November 2014 (93 months), we find that: Keep Reading

Momentum-driven Turn-of-the-month Effect in Commodity Futures

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

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