Most financial asset returns exhibit volatility persistence. We investigate this phenomenon in the context of daily returns in commodity futures markets. We show that the time gap between the arrival of news to the markets and the delivery time of futures contracts is the fundamental variable in...

We apply a new Bayesian approach to multiple‐contract futures data. It allows the volatility of futures prices to depend upon physical inventories and the contract's time to delivery—and it allows those parametric effects to vary over time. We investigate price movements for lumber contracts...

We study the difference in the volatility dynamics of CBOT corn, soybeans, and oats futures prices across different delivery horizons via a smoothed Bayesian estimator. We find that futures price volatilities in these markets are affected by inventories, time to delivery, and the crop progress...

We study the difference in the volatility dynamics of CBOT corn, soybeans, and oats futures prices across different delivery horizons via the smoothed Bayesian estimator of Karali, Dorfman, and Thurman (2010). We show that the futures price volatilities in these markets are affected by the...

We investigate how lumber futures returns are affected by monthly housing starts announcements and analyze the dependence of the response on lumber inventories and time to delivery. To do so we develop a generalized least squares (GLS) method to jointly analyze simultaneously traded contracts....

Replaced with revised version of paper on 06/11/07.

Replaced with revised version of paper 07/15/08.

We analyze the determinants of daily futures price volatility in corn, soybeans, wheat, and oats markets from 1986 to 2007. Combining the information from simultaneously traded contracts, a generalized least squares method is implemented that allows us to clearly distinguish among...

Hedging is one of the most important risk management decisions that farmers make and has a potentially large role in the level of profit eventually earned from farming. Using panel data from a survey of Georgia farmers that recorded their hedging decisions for four years on three crops we...

We estimate a model of common and commodity-specific, high- and low-frequency factors, built on the spline-GARCH model of Engle and Rangel (2008) to explain the period of exceptionally high price volatility in commodity markets during 2006-2008. We find that decomposing realized volatility into...