Joseph P. Janzen

Hedging and Speculative Trading in Agricultural Futures Markets

Regulators and industry participants have expressed concern that excessive speculation harms agricultural futures markets. Such harm may arise if speculators cause prices to systematically differ from the price sequence that would arise in markets populated by equally informed traders with rational expectations (RE). We show theoretically that, when traders exhibit differences of opinion (DO) about the expected value of the commodity, futures prices may diverge from the RE equilibrium. Moreover, we develop a testable prediction, namely that positions held by different trader groups are correlated with prices in a DO equilibrium but not correlated in a RE equilibrium. We find strong empirical support for the DO-type environment; changes in positions held by managed money traders are positively correlated with prices, and changes in positions held by producers are negatively correlated. In the context of our DO model, this finding implies that prices change by more on average than producers think they should and by less than managed money thinks they should. However, the evidence suggests that neither group is systematically more prescient than the other.

Deconstructing Wheat Price Spikes: A Model of Supply and Demand, Financial Speculation, and Commodity Price Comovement

In 2008, wheat futures prices spiked and then crashed along with prices for other agricultural and nonagricultural commodities. Market observers offered several theories to explain this common movement, or comovement, in prices, and have proposed policies to address the perceived problem of excessive price volatility. The design of an appropriate policy response would benefit from a better understanding of the cause of the observed price movements. This study uses an econometric model to decompose observed wheat prices into a set of economic factors and measure the relative contribution of each factor to observed price changes. Findings show that market-specific shocks related to supply and demand for wheat were the dominant cause of price spikes in the three U.S. wheat futures markets. Fluctuations in the global macroeconomy associated with broadbased demand shocks were relatively less significant for wheat than for other commodities like crude oil and corn. Finally, little evidence suggests commodity index trading contributed to recent price spikes.

Futures Prices in Supply Analysis: Are Instrumental Variables Necessary?

Crop yield shocks are partially predictable-high planting-time futures prices have tended to indicate that yield would be below trend. As a result, regressions of total caloric production on futures prices produce estimates of the supply elasticity that are biased downwards by up to 75%. Regressions of the worlds growing area on futures prices have a much smaller bias of about 20% because although yield shocks are partially predictable, this predictability has a relatively small effect on land allocation. We argue that the preferred method for estimating the crop supply elasticity is to use regressions of growing area on futures prices and to include the realized yield shock as a control variable. An alternative method for bias reduction is to use instrumental variables (IVs). We show that the marginal contribution of an IV to bias reduction is small-IVs are not necessary for futures prices in supply analysis.

Commodity Price Comovement and Financial Speculation: The Case of Cotton

&NA; Recent booms and busts in commodity prices have generated concerns that financial speculation causes excessive commodity‐price comovement, driving prices away from levels implied by supply and demand under rational expectations. We develop a structural vector autoregression model of a commodity futures market and use it to explain two recent spikes in cotton prices. In doing so, we make two contributions to the literature on commodity price dynamics. First, we estimate the extent to which cotton price booms and busts can be attributed to comovement with other commodities. Finding such comovement would be necessary but would not be sufficient evidence to establish that broad‐based financial speculation drives commodity prices. Second, after controlling for aggregate demand and comovement, we develop a new method to point identify shocks to precautionary demand for cotton separately from shocks to current supply and demand. To do so, we use differences in volatility across time implied by the rational expectations competitive storage model. We find limited evidence that financial speculation caused cotton prices to spike in 2008 or 2011. We conclude that the 2008 price spike was driven mostly by precautionary demand for cotton, and the 2011 spike was caused by a net supply shortfall.