Journal of Agricultural and Resource Economics
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We compare the accuracy of crop condition models from existing literature in forecasting U.S. corn and soybean yields. The data for the study consists of weekly state and national crop condition ratings from the USDA over 1986 through 2022. A battery of statistical tests is applied to perform out-of-sample forecasts over 2000 through 2022. While there are differences in the accuracy of the models, test results are uniform in suggesting that no model has statistically significant superior forecast accuracy. A key finding is that relatively simple models perform just as well as more complex and computationally demanding models.
Using traditional price volatility tests, we find that the market impact of USDA Cattle on Feed and Hogs and Pigs reports largely disappeared after 2000. In contrast, using market surprise tests, we find no evidence that the impact of Cattle on Feed information changed significantly after 2000. The evidence is mixed for Hogs and Pigs reports using market surprise tests, with market inventory information increasing in value and breeding inventory decreasing. The contrasting results can be explained by increasing market concentration in cattle and hogs leading to smaller market surprises and smaller futures price reactions.
This paper investigates whether the accuracy of outlook hog price forecasts can be improved using composite forecasts in an out-of-sample context. Price forecasts from four widely-recognized outlook programs are combined with futures-based forecasts, ARMA, and unrestricted Vector Autoregressive (VAR) models. Quarterly data are available from 1975.I through 2007.IV for Illinois/Purdue and 1975.I-2010.IV for Iowa, Missouri, and USDA forecasts, which allow for a relatively long out-of-sample evaluation after permitting model specification and appropriate composite-weight training periods. Results show that futures and numerous composite procedures outperform outlook forecasts, but no-change forecasts are inferior to outlook forecasts. At intermediate horizons, OLS composite procedures perform well. The superiority of futures and composite forecasts decreases at longer horizons except for an equal-weighted approach. Importantly, with few exceptions, nothing outperforms the equal-weight approach significantly in any program or horizon. In addition, the equal-weight approach as well as other composite approaches can generally produce larger trading profits compared to outlook forecasts. Overall, findings favor the use of equal-weighted composites, consistent with previous empirical findings and recent theoretical papers.
This paper examines returns from holding 30- and 90-day call and put positions, and the forecasting performance of implied volatility in the live and feeder cattle options markets. Implied volatility is an upwardly biased and inefficient predictor of realized volatility, with bias most pronounced in live cattle. While significant returns exist from several positions, strategies are strongly affected by drifts in futures prices. However, returns from live cattle puts are persistent, and evidence from 30-day straddle returns indicates the live cattle market overprices volatility. Overpricing is consistent with volatility risk, the effect of which is magnified by extreme market conditions.
As agricultural options markets grow, perceptions of overpricing persist among market participants. This study tests the efficiency of corn, soybean, and wheat options by computing trading returns. Several call and put option strategies yield significant profits, but returns are influenced by movements in the futures price, and straddle trading does not lead to significant returns. The combined analysis of put, call, and straddle returns indicates that significant returns can be attributed to drifts in the underlying futures, and that the corn, soybean, and wheat options markets are efficient.
This study uses quantile regressions to estimate historical forecast error distributions for WASDE forecasts of corn, soybean, and wheat prices, and then compute confidence limits for the forecasts based on the empirical distributions. Quantile regressions with fit errors expressed as a function of forecast lead time are consistent with theoretical forecast variance expressions while avoiding assumptions of normality and optimality. Based on out-of-sample accuracy tests over 1995/96–2006/07, quantile regression methods produced intervals consistent with the target confidence level. Overall, this study demonstrates that empirical approaches may be used to construct accurate confidence intervals for WASDE corn, soybean, and wheat price forecasts.
The forecasting content of the Commodity Futures Trading Commission’s Commitments of Traders (COT) report is investigated. Bivariate Granger causality tests show very little evidence that traders’ positions are useful in forecasting (leading) returns in 10 agricultural futures markets. However, there is substantial evidence that traders respond to price changes. In particular, noncommercial traders display a tendency for trend following. The other trader classifications display mixed styles, perhaps indicating those trader categories capture a variety of traders. The results generally do not support use of the COT data in predicting price movements in agricultural futures markets.
This study investigates the impact of six major USDA reports in hog and cattle markets: Cattle; Cattle on Feed; Cold Storage; Hogs and Pigs; Livestock, Dairy, and Poultry Outlook (LDPO); and World Agricultural Supply and Demand Estimates (WASDE). A TARCH-in-mean model, with dummy variables to measure the impact of USDA reports and other external factors, is used to model close-to-open live-lean hog and live cattle futures returns from January 1985 through December 2004. The analysis revealed a statistically significant impact of all but Cattle and Cold Storage reports in live/lean hog futures, and all but Cold Storage reports in live cattle futures. Hogs and Pigs reports had the highest impact on live/lean hog returns by increasing conditional standard deviation 96%. Cattle, Cattle on Feed, and Hogs and Pigs reports had the highest impact on live cattle returns by increasing conditional standard deviation between 26% and 37.5%.
A conceptual framework is developed which provides insight into the factors affecting the impact of market advisory service (MAS) recommendations on producer pricing decisions. Data from a survey of 656 U.S. producers reveal that the perceived performance of the MAS, the way in which MAS recommendations are delivered, as well as the match between MAS and producers’ marketing philosophy, are important factors explaining the impact of MAS recommendations. Risk attitude does not affect the impact of MAS recommendations on producers’ decisions, suggesting producers are more interested in the price-enhancing characteristics of MAS advice than in its risk-reducing features. Key words: market advisory services, ordered probit model, producers’ marketing decisions
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