Commodity Futures

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

Do variations in the Baltic Dry Index (BDI), a weighted average of the Baltic Exchange shipping cost indexes for the four largest dry-vessel classes, serve as an early measure of global demand for raw materials and thereby predict asset class returns? In the January 2011 version of their paper entitled “The Baltic Dry Index as a Predictor of Global Stock Returns, Commodity Returns, and Global Economic Activity”, Gurdip Bakshi, George Panayotov and Georgios Skoulakis investigate the ability of BDI to predict stock market and commodity market returns. They focus on three-month changes in BDI as a predictor to smooth the high volatility of the monthly series. Using monthly BDI levels and returns for four MSCI regional stock indexes, 19 developed country stock indexes, 12 emerging country stock indexes, three spot commodity indexes and industrial production data for 20 countries mostly over the period May 1985 through September 2010 (305 months), they find that: Keep Reading

Do changes in the level of futures markets activity predict returns for corresponding asset classes? In their January 2011 paper entitled “What Does Futures Market Interest Tell Us about the Macroeconomy and Asset Prices?”, Harrison Hong and Motohiro Yogo relate futures markets open interest (the number of contracts outstanding) to future asset class returns. They focus on the 12-month change in open interest and 12-month future return. As noted by the authors, simple logic suggests that open interest should be a non-directional because each futures contract involves countering long and short positions. However, changes in the number of futures contracts could indicate changes in anticipated economic risks. Using monthly open interest data for 30 commodity futures, eight currency futures, ten bond futures, 14 stock index futures and corresponding asset class returns for periods from earliest availability of data through 2008, they find that: Keep Reading

If you believe inflation is coming, should you shift assets toward commodities-oriented assets? In their November 2010 paper entitled “Are Commodities a Good Hedge Against Inflation? A Comparative Approach”, Laura Spierdijk and Zaghum Umar compare five measures of inflation hedging capacity as applied to commodities for investment horizons ranging from one month to ten years. They also investigate how these measures of hedging capacity relate. Using the monthly U.S. inflation rate based on the seasonally adjusted all urban Consumer Price Index, monthly returns for the S&P GSCI Total Return Index (proxy for a diversified, unleveraged, long-only commodity futures position) and its components, and the U.S. 3-month Treasury bill (T-bill) yield during January 1982 through August 2010, they find that: Keep Reading

What is the best way to measure and interpret market reaction to new information? In their October 2010 paper entitled “Measuring Flow Toxicity in a High Frequency World”, David Easley, Marcos López de Prado and Maureen O’Hara introduce a new method to estimate the degree to which trading in financial markets is informed. They name this metric Volume-Synchronized Probability of Informed Trading (VPIN), approximated by the fraction of trading volume that is imbalanced (absolute difference between seller-initiated and buyer-initiated volumes, divided by total volume).  Their approach builds on three beliefs: (1) new orders indicate arrival of new information potentially predictive of subsequent price moves; (2) a specific volume of trades therefore represents a more consistent metric for information arrival than an interval of time; and, (3) a trade imbalance is the hallmark of arrival of important information. In a related November 2010 paper entitled “The Microstructure of the ‘Flash Crash’: Flow Toxicity, Liquidity Crashes and the Probability of Informed Trading”, these same authors focus this method on the May 6, 2010 market crash. Using high-frequency (one-minute intervals) price and volume data for a variety of futures contracts during January 2008 through August 2010 to construct rolling sets of equal-volume increments, they find that: Keep Reading

Are there commodity futures traders who consistently outperform? Who are they? What information do they exploit? In the September 2010 version of their paper entitled “Identifying Informed Traders in Futures Markets”, Raymond Fishe and Aaron Smith examine the short-term trading abilities of commodity futures traders by recreating their trading histories. They distinguish between those who trade intraday and those who hold overnight, arguing that the latter are efficient processors of technical trading information, while the former possess the best signals about fundamental short-run price pressures. Using daily positions for 8,921 traders in 12 futures markets over the period January 2000 through May 2009, they find that: Keep Reading

How effectively and consistently do equities, bonds, oil, gold and the dollar serve as hedges and safe havens for each other? In their September 2010 paper entitled “Hedges and Safe Havens – An Examination of Stocks, Bonds, Oil, Gold and the Dollar”, Cetin Ciner, Constantin Gurdgiev and Brian Lucey investigate pairwise hedging and safe haven relationships among these five major assets/asset classes. The define an asset as a hedge (safe haven) for another if respective returns are uncorrelated or negatively correlated on average over the long term (during relatively short intervals of stress). They define the long term (relatively short intervals) as their entire sample period (rolling four-month subperiods). They define intervals of stress as returns in the lowest fourth of observations. Using daily levels of the S&P 500 Index, an index of 10-year Treasuries, nearest-month gold and oil futures and the Federal Reserve Nominal Trade Weighted Effective Index for the dollar from January 1985 through October 2009 (nearly 25 years), they find that: Keep Reading