Commodity Futures

Can investors exploit idiosyncratic volatility exhibited by commodity futures? In their December 2011 paper entitled “Idiosyncratic Volatility Strategies in Commodity Futures Markets”, Adrian Fernancez-Perez, Ana-Maria Fuertes and Joelle Miffre investigate the usefulness of idiosyncratic volatility as a predictor of commodity futures returns. They define idiosyncratic volatility of commodity futures as return volatility not explained by contemporaneous variation in hedging pressure. They calculate hedging pressure from CFTC Commitments of Traders reports by relating long positions to total positions across trader categories. Return calculations assume: (1) holding the first nearby contract up to one month before maturity and then rolling to the next-nearest contract; (2) trading on a fully collateralized basis, meaning that half of trading capital earns the risk-free rate (three-month Treasury bill yield); and, (3) reporting only returns in excess of the risk-free rate, which averages about 3.3% annually over the sample period. They test all combinations of commodity ranking (whether for idiosyncratic volatility, return momentum or roll return) and portfolio holding intervals of 4, 13, 26 and 52 weeks. They calculate alpha by regressing long-short commodity futures portfolio returns against the same-interval hedging pressure risk premium. Using Friday settlement prices of nearest and second-nearest contracts for 27 commodity futures and weekly hedging pressure data during September 30, 1992 through March 25, 2011, they find that: Keep Reading

How many commodity sectors are relevant for portfolio diversification planning, and how do their behaviors differ? In their December 2011 paper entitled “How Many Commodity Sectors Are There, and How Do They Behave?”, Geetesh Bhardwaj and Adam Dunsby examine the statistical properties of commodity futures prices to discover natural sectors and investigate how returns for these sectors behave under different market conditions. They estimate commodity futures returns based on continually rolling at the end of each month to a long position in the nearest contract that does not have first notice day or expiration date during the next month. They measure all returns as “excess” relative to the one-month Treasury bill yield. They define economic expansions and recessions based on National Bureau of Economic Research (NBER) business cycle dates. They define extreme conditions for economic conditions and the stock market as 5% tails. Using monthly futures-only returns for May 1990 through September 2011 and spot returns as available to extend price histories back through the 1970s for 25 individual commodities, monthly returns for stocks (S&P 500 Index), U.S. bond indexes and the U.S. dollar index and several contemporaneous economic measurements, they find that: Keep Reading

Do calendar effects found in stock markets also appear in broad stock index futures? In their November 2011 paper entitled “Calendar Anomalies in Stock Index Futures”, Oscar Carchano and Angel Pardo investigate 188 possible cyclical anomalies in S&P 500, DAX and Nikkei index futures contracts (derived from day-of-the-week, month-of-the-year, weekday-of-the-month, week-of-the-month, semi-month, turn-of-the-month, end-of-year, holidays, semi-month-of-the-year, week-of-the-month-of-the-year, Friday the 13th, Halloween effect and quarterly futures expiration). They note that small trading frictions and ease of shorting promote exploitability of anomalies in futures markets. They assume round trip trading frictions of 0.05% for assessing net profitability. Applying tests not dependent on type of return distribution to stock index futures prices from December 1991 through April 2008, they find that: Keep Reading

An array of leveraged exchange-traded funds (ETF) track short-term (daily) changes in commodity and currency exchange indexes. Over longer holding periods, these ETFs tend to veer off track. The cumulative veer can be large. How do leveraged ETFs perform over a multi-year period? What factors contribute to their failure to track underlying indexes? To investigate, we consider a set of 12 ProShares 2X leveraged index ETFs (six matched long-short pairs), involving a commodity index, oil, gold, silver and the euro-dollar and yen-dollar exchange rates, with the start date of 12/9/08 determined by inception of the youngest of these funds (Ultra Yen). Using daily dividend-adjusted prices for these funds over the period 12/9/08 through 11/4/11 (almost three years), we find that: Keep Reading

Do oil price variations reliably affect returns for U.S. industry-level stock portfolios? In the June 2011 draft of their paper entitled “U.S. Industry-Level Returns and Oil Prices”, Qinbin Fan and Mohammad Jahan-Parvar apply several tests to investigate how oil price changes impact stock returns for 49 U.S. industries. They test economic significance by: (1) using a 60-month rolling historical window to model the predictive relationship between spot oil price changes and industry returns; (2) applying this relationship each month to the last observed oil price change to predict future industry returns; and, (3) investing in either industry portfolios or 4-week Treasury bills according to which has the higher expected return. They assume an industry portfolio-Treasury bill switching friction of 0.10%. Using monthly and weekly prices for West Texas Intermediate crude oil spot (January 1979 through January 2009) and nearest contract Cushing, Oklahoma light sweet crude oil futures (February 1986 through January), along with contemporaneous U.S. industry returns, they find that: Keep Reading

What is the best way to incorporate commodities into a diversified portfolio? In her August 2011 paper entitled “Long-Short Commodity Investing: Implications for Portfolio Risk and Market Regulation”, Joelle Miffre studies the performance of long-short commodity strategies and their hedging properties with respect to traditional asset classes (proxied by the S&P 500 Index and Barclays Capital US Aggregate Bond Index), especially during crises. She calculates futures contract returns assuming that investors hold the nearest contract until one month to maturity and then roll to the second nearest contract. She considers four single-sort and four double-sort long-short strategies based on past return (momentum), roll return (term structure), positions of hedgers and positions of speculators. All eight strategies systematically take long (short) positions in commodities expected to appreciate (depreciate) based on these indicators. Using weekly (Friday) data for 27 commodity futures (12 agricultural, five energy, four livestock and five metal), the S&P-GSCI, the selected traditional asset class proxies and CFTC Commitments of Traders reports on positions of hedgers and speculators during October 1992 through March 2011, she finds that: Keep Reading

Has the strong appreciation of gold since 2001 produced a price bubble? In their March 2011 paper entitled “Is There a Speculative Bubble in the Price of Gold?”, Jedrzej Bialkowski, Martin Bohl, Patrick Stephan and Tomasz Wisniewski measure deviations of actual gold price from its fundamental value to identify gold bubbles. They use the convenience yield model and associated monthly commodity “dividends” (benefit of holding gold rather than gold futures) to derive gold’s fundamental value. They then apply a regime-switching test to estimate whether deviations of actual gold price from fundamental value enter bubble territory over their sample period. Using daily gold spot and nearby futures contract prices and the Treasury bill yield (risk-free rate) during November 1978 through March 2010 (377 months), they find that: Keep Reading

Can speculators in agricultural commodity futures earn a reliable premium from those seeking to hedge agricultural industry risk? In other words, can traders systematically exploit a persistent backwardation of agricultural commodity futures contracts? In the May 2011 version of their paper entitled “Returns to Traders and Existences of a Risk Premium of a Risk Premium in Agricultural Futures Markets”, Nicole Aulerich, Scott Irwin and Philip Garcia investigate whether hedgers pay speculators for protection against adverse price movements in 12 agricultural commodity futures markets (cocoa, coffee, corn, cotton, feeder cattle, lean hogs, live cattle, soybeans, soybean oil, sugar, CBOT wheat and KS wheat). They focus on the performance of commodity index traders (emerging in 2004), who should expose this risk premium by passively holding positions opposite of hedgers. Using end-of-day prices and positions data for 12 agricultural commodity futures contracts and options by type of trader (commercial hedger, large speculators, commodity index trader and small traders) during January 2000 through September 2009 (a period of large price changes), they find that: Keep Reading

How do the daily price statistics of commodities differ, and how do they compare with those for equities? In their May 2011 paper entitled “The Dynamics of Commodity Prices”, Chris Brooks and Marcel Prokopczuk examine the daily price statistics for six major commodity markets (crude oil, gasoline, gold, silver, soybeans and wheat) individually and relative to each other and the equity market. Using daily spot prices for the commodities and daily levels of the S&P 500 Index for January 1985 through March 2010 (over 25 years), they find that: Keep Reading

Are some stocks more sensitive to commodity prices than others? If so, is the variation exploitable? In their February 2011 paper entitled “The Stock Market Price of Commodity Risk”, Martijn Boons, Frans de Roon and Marta Szymanowska investigate the cross-sectional variation in stock returns associated with commodity price changes by calculating betas for individual stocks and industry portfolios relative to a broad open interest-weighted commodity futures index. They calculate commodity futures index returns based on a nearest-to-maturity rollover. They calculate stock betas against this return series (commodity betas) each month based on rolling 60-month historical regressions. They then form 25 value-weighted portfolios each month based on the intersections of independent lagged commodity beta and lagged size quintile rankings and use these portfolios to measure future return implications of commodity beta. Using monthly futures price and open interest data for 33 liquid commodities from 1975 (when futures prices for at least 20 commodities are available) through 2008, along with contemporaneous data for a broad sample of U.S. stocks and 48 industry portfolios, they find that: Keep Reading