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Commodity Futures

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

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

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

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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