Commodity Futures

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

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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

Best Safe Haven ETF?

A subscriber asked which exchange-traded fund (ETF) asset class proxies make the best safe havens for the U.S. stock market as proxied by the S&P 500 Index. To investigate, we consider the the following 12 ETFs as potential safe havens:

Utilities Select Sector SPDR ETF (XLU)
SPDR Dow Jones REIT ETF (RWR)
iShares 20+ Year Treasury Bond (TLT)
iShares 7-10 Year Treasury Bond (IEF)
iShares 1-3 Year Treasury Bond (SHY)
iShares Core US Aggregate Bond (AGG)
iShares TIPS Bond (TIP)
SPDR Gold Shares (GLD)
PowerShares DB Commodity Tracking ETF (DBC)
United States Oil (USO)
iShares Silver Trust (SLV)
PowerShares DB G10 Currency Harvest ETF (DBV)

We consider three ways of testing these ETFs as safe havens for the U.S. stock market based on daily, weekly and monthly return measurement frequencies:

  1. Contemporaneous return correlation with the S&P 500 Index during all market conditions.
  2. Return/performance during S&P 500 Index bear markets as specified by the index being below its 200-day/40-week/10-month simple moving average (SMA) for the prior measurement interval.
  3. Return/performance during S&P 500 Index bear markets as specified by the index being in drawdown from a prior high-water mark by more than some percentage (baseline -10%) for the prior measurement interval.

Using daily, weekly and monthly dividend-adjusted closing prices for the 12 ETFs from their respective inceptions through January 2016, and contemporaneous daily, weekly and monthly levels of the S&P 500 Index from 10 months before the earliest ETF inception through January 2016, we find that: Keep Reading

Overview of Commodity Futures Investment Strategies

What kinds of commodity futures portfolio allocation strategies work? In her December 2015 paper entitled “Long-Short Commodity Investing: A Review of the Literature”, Joelle Miffre summarizes recent academic studies that analyze the performance of long-short commodity futures strategies. She focuses on strategies exploiting roll yields, inventory levels, hedging pressure or momentum. She also surveys alternative strategies based on risk, value, liquidity, sensitivity to inflation or skewness, plus some combination strategies. She relies mostly on Sharpe ratio to compare strategies. Based on results from about 50 studies, she concludes that: Keep Reading

When Carry, Momentum and Value Work

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

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

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