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Commodity Futures

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

Hedging Inflation with Commodities

Can investors rely on commodities to protect against inflation? In their May 2013 paper entitled “Commodities as Inflation Protection”, Andrew Marks, George Crawford and Jim Kyung-Soo Liew examine the belief that commodities represent an intrinsic store of value that hedges against inflation. They use spot prices for commodities because these prices have long histories. They use the U.S. Consumer Price Index (CPI) as the measure of inflation. However, CPI is an aggregate of the prices of many finished goods and services adjusted to reflect a government model of the actual cost of living, so it may not be possible to create a portfolio that tracks its composition. Commodities (such as crops, crude oil and precious metals), as raw inputs to these goods and services, may rise in price with the CPI, and the proliferation of futures contracts and related exchange-traded funds (ETF) makes investing in commodities accessible. Using monthly spot prices for 45 individual commodities and 14 aggregating indexes during January 1960 through December 2012 (53 years), with focus on five subperiods involving relatively high inflation, they find that: Keep Reading

Intrinsic Value and Momentum Across (Futures) Asset Classes

Do time series carry (intrinsic value) and time series momentum (intrinsic momentum) strategies work across asset classes? What drives their returns, and how do they interact? In the January 2013 very preliminary version of their paper entitled “The Returns to Carry and Momentum Strategies: Business Cycles, Hedge Fund Capital and Limits to Arbitrage”, Jan Danilo Ahmerkamp and James Grant examine intrinsic value strategy and intrinsic momentum strategy returns for 55 worldwide futures contract series spanning equities, bonds, currencies, commodities and metals, including the effects of business cycle/economic conditions and institutional ownership. They study futures (rather than spot/cash) markets to minimize trading frictions and avoid shorting constraints. They calculate futures contract returns relative to the nearest-to-maturity futures contract (not spot/cash market) price. The momentum signal is lagged 12-month cumulative raw return. The carry (value) signal is the lagged 12-month average normalized price difference between second nearest-to-maturity and nearest contracts. They test strategies that are each month long (short) contracts with positive (negative) value or momentum signals. They also test a combination strategy that is long (short) contracts with both value and momentum signals positive (negative). For comparability of assets, they weight contract series within multi-asset portfolios by inverse volatility, estimated as the average absolute value of daily returns over the past three months. Their benchmark is a long-only portfolio of all contracts weighted by inverse volatility. Using daily settlement prices for the nearest and second nearest futures contracts of the 55 series (10 equities, 12 bonds, 17 commodities, nine currencies and seven metals) as available during 1980 through 2012, they find that: Keep Reading

A Few Notes on A Trader’s First Book on Commodities

In her 2012 book A Trader’s First Book on Commodities: An Introduction to The World’s Fastest Growing Market (2nd Edition), author Carley Garner hopes to convey “the realization that anything is possible in the commodity markets. Never say ‘never’ — if you do, you will eventually be proven wrong. Additionally, trading the markets is an art, not a science. Unfortunately, there are no black-and-white answers, nor are there fool-proof strategies — but that does not mean that there aren’t opportunities.” Her further hope is that “this book is the first step in your journey toward victory in the challenging, yet potentially rewarding, commodity markets.” Some notable points from the book are: Keep Reading

Crude Oil as Safe Haven During Wars

Wars both consume crude oil and potentially disrupt supplies. Do they reliably drive up oil price? In their November 2012 paper entitled “Crude Oil as a Safe Haven Asset in Times of War”, Tomasz Wisniewski and Ayman Omar examine the behaviors of crude oil price and stock market indexes around severe international crises and wars. They construct a sample of crises from the July 2010 version of the International Crisis Behavior (ICB) database, excluding events scoring below “6” on the severity scale (such as protests, diplomatic sanctions and withholding economic aid). They also extract a war subsample (border clash/crossing by military forces, invasion of air space, sea/air military operations and large-scale military attacks/bombing). They use the Cushing, Oklahoma West Texas Intermediate (WTI) spot price as crude oil price, but also test the Brent spot price as a robustness check. They consider S&P 500 and MSCI World as representative stock market indexes. They define the crisis/war impact interval as 50 trading days before through 50 trading days after outbreak. They define the effect of a crisis/war on price as the “abnormal” return compared to price behavior during the 150 trading days prior to the impact interval. Using daily crude oil spot price and stock index levels around 64 instances of severe international crises and 43 wars during January 1987 through December 2007, they find that: Keep Reading

“Real” Assets and Inflation

Which asset class best hedges inflation? In the September 2012 draft of his book chapter entitled “‘Real’ Assets”, Andrew Ang examines the behaviors of the following assets commonly thought to hold their value during times of high inflation (“real” assets): inflation-linked bonds, commodities, real estate and U.S. Treasury bills (T-bill). He focuses on inflation as year-over-year change in the U.S. Consumer Price Index for all urban consumers and all items, but considers also inflation rates for medical care and higher education. He distinguishes inflation hedging (measured by correlation of returns and inflation) from long-run asset class performance. Using asset class proxy returns and U.S. inflation rates as available through 2011, he finds that: Keep Reading

Diversification Power of Commodities

Are commodities effective diversifiers for stocks and bonds? In his September 2012 paper entitled “Commodity Investments: The Missing Piece of the Portfolio Puzzle?”, Xiaowei Kang examines the diversification properties of commodity indexes relative to stock and bond indexes. He focuses on the widely used S&P GSCI, composed of 24 commodities with liquid futures markets weighted by world production value. He also considers the S&P GSCI Dynamic Roll, designed to suppress negative roll returns by rolling into longer-dated (nearby) futures contracts when a commodity’s term structure is in contango (backwardation). Using monthly levels of these indexes, MSCI World (to represent stocks) and Barclays Global Aggregate Bond Index (to represent bonds), along with contemporaneous U.S. Treasury bill yields to calculate excess returns, from as early as December 1970 through June 2012, he finds that: Keep Reading

Managed Futures as Portfolio Diversifier

Are managed futures programs good portfolio diversifiers? In his September 2012 paper entitled “Revisiting Kat’s Managed Futures and Hedge Funds: A Match Made in Heaven”, Thomas Rollinger updates prior research exploring the diversification effects of adding managed futures to traditional portfolios of stocks and bonds and to portfolios including stocks, bonds and hedge funds. His proxies for the four asset classes are: (1) for stocks, the S&P 500 Total Return Index; (2) for bonds, the Barclays U.S. Aggregate Bond Index; (3) for hedge funds, the HFRI Fund Weighted Composite Index; and, (4) for managed futures programs, the Barclay Systematic Traders Index (focused on systematic trend-following strategies). He assumes monthly (frictionless) portfolio rebalancing. Using monthly returns for the four asset class indexes during June 2001 through December 2011, he finds that: Keep Reading

Crude Oil and Natural Gas Prices Reliably Intertwined?

In mid-2008, a reader speculated and asked: “You have probably heard of the historical 6:1 crude oil/natural gas price ratio. This relationship is said to be mean reverting based on the thermal equivalence of the two commodities. Does this ratio have any predictive power for the future prices of oil or natural gas? If there is no predictive power for this ratio, then it could mean that the thermal equivalence itself shifts over time. And hedge funds who are long natural gas right now are making a huge fundamental mistake.” If there are relationships, we hypothesize that a high (low) crude oil-natural gas price ratio should predict future changes in the prices of natural gas of crude oil to decrease (increase) the ratio. Using the monthly composite U.S. refiner cost of crude oil (nominal dollars per barrel) and the monthly U.S. wellhead natural gas price (nominal dollars per thousand cubic feet) for January 1976 through June 2012 (438 months), we find that: Keep Reading

COT Data Predictive for S&P 500 Index?

The zero-sum S&P 500 futures/options market involves three groups of traders: (1) commercial hedgers; (2) non-commercial traders (large speculators); and, (3) non-reportable traders (small or retail speculators) representative of the public. The Commodity Futures Trading Commission (CFTC) collects and publishes aggregate positions (short, long and spread) for each group in a weekly Commitment of Traders (COT) report. CFTC releases reports on Fridays for positions as of the preceding Tuesdays. Are the behaviors of these groups in trading S&P 500 index futures/options reliable indicators of future stock market direction? To investigate, we relate weekly S&P 500 Index futures/options short-long ratios for the three trader categories to S&P 500 Index returns. Using historical weekly COT report data for S&P 500 Index futures and options combined and corresponding weekly dividend-adjusted prices for SPDR S&P 500 (SPY) as a tradable proxy for the index during March 1995 (the earliest available COT data) through early September 2012 (912 weeks), we find that: Keep Reading

Evolution of Commodity Futures Indexes

Does the latest generation of commodity futures indexes, which systematically exploits both backwardation and contango, outperform its predecessors? In her July 2012 paper entitled “Comparing First, Second and Third Generation Commodity Indices”, Joelle Miffre reviews the evolution of commodity futures indexes and assesses the performance of three groups of these indexes: (1) first generation, which are long-only and generally ignore backwardation and contango; (2) second generation, which are also long-only but attempt to mitigate contango while exploiting backwardation; and, (3) third generation, which are long-short to exploit both backwardation and contango. Using monthly levels of 6 first, 23 second and 9 third generation commodity futures indexes from the end of May 2008 through April 2012, she finds that: Keep Reading

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