Linwood A. Hoffman

Unbiasedness and Market Efficiency Tests of the U.S. Rice Futures Market

This study examines short-run and long-run unbiasedness within the U.S. rice futures market. Standard OLS, cointegration, and error-correction models are used to determine unbiasedness. In addition, the forecasting performance of the rice futures market is analyzed and compared to out-of-sample forecasts derived from an additive ARIMA model and the error-correction model. The results of our unbiasedness tests and the forecasting performance of the rice futures market provide supporting evidence that the U.S. long-grain rough rice futures market is efficient. The results have important price risk management and price discovery implications for Arkansas and U.S. rice industry participants.

Using Futures Prices to Forecast US Corn Prices: Model Performance with Increased Price Volatility

A futures price forecasting model is presented which uses monthly futures prices, cash prices received, basis values (cash prices less futures) and marketing weights to forecast the season-average farm price for US corn. Performance of the model forecasts is examined using standard measures, such as mean absolute error, mean absolute percentage error and mean squared error. Tests for statistical differences between the futures model forecast and price projections from the US Department of Agriculture (USDA) are conducted using the Modified Diebold–Mariano test statistic. A measurement of price volatility identified the past 3 crop years, 2006/2007–2008/2009 with increased volatility compared to the prior 6 years, 2000/2001–2005/2006. Forecast errors from the futures forecast model increased during these volatile price years compared to the prior 6 year period which exhibited more stability. Suggestions are made to improve model price forecasts during periods of price volatility.

Volatility Spillover and Time-Varying Conditional Correlation Between DDGS, Corn, and Soybean Meal Markets

In this paper, we seek to identify the price and volatility transmission mechanisms between DDGS, corn, and soybean meal markets to better inform market participants who wish to manage price risks in these three markets. Using weekly data from January 2000 to May 2016, we find important interplays between the three markets in both price and volatility dynamics. We uncover a stable long-run equilibrium between the three prices, and find that in the long-run, DDGS prices are positively correlated with both corn and soybean meal prices. However, corn and soybean meals are weakly exogenous — neither markets respond to deviations from this long-run relationship. We also find strong time-varying dynamic conditional correlations between the three markets, with the correlation between DDGS and corn strengthening between 2006 and 2012, the period when ethanol production in the US underwent massive expansion. Additionally, we identify significant volatility spillovers from both the corn and soybean meal markets to the DDGS market, with the impact from corn shocks much larger compared to soybean meal shocks. Corn appears to be the primary channel through which exogenous shocks are transmitted to the DDGS market. Our results also suggest that neither DDGS prices nor volatility significantly impact the corn and soybean meal markets.