U.S. Cotton Prices and the World Cotton Market: Forecasting and Structural Change

The purpose of this study was to analyze structural changes that took place in the cotton industry in recent years and develop a statistical model that reflects the current drivers of U.S. cotton prices. Legislative changes authorized the U.S. Department of Agriculture to resume publishing cotton price forecasts for the first time in 79 years. In addition, systematic problems have become apparent in the forecasting models used by USDA and elsewhere, highlighting the need for an updated review of price relationships. This study concluded that a structural break in the U.S. cotton industry occurred in 1999, and that world cotton supply has become an important determinant of U.S. cotton prices. China’s trade and production policy also continues to be an important factor in price determination. The model developed here forecasts changes in the U.S. upland cotton farm price based on changes in U.S. cotton supply, changes in U.S. stocks-to-use ratio (S/U), changes in China’s net imports as a share of world consumption, selected farm policy parameters, and changes in the foreign supply of cotton.forecasting, cotton, price, demand, trade, structural change, farm programs., Demand and Price Analysis, Q100, Q110, Q130,

U.S. Cotton Prices and the World Cotton Market; Forecasting and Structural Change

This report analyzes recent structural changes in the world cotton industry and develops a statistical model that reflects current drivers of U.S. cotton prices. Legislative changes in 2008 authorized USDA to resume publishing cotton price forecasts for the first time in nearly 80 years. Systematic problems have become apparent in the forecasting models used by USDA and elsewhere, highlighting the need for an updated review of price relationships. A structural break in the U.S. cotton industry occurred in 1999, and world cotton supply has become an important determinant of U.S. cotton prices, along with China’s trade and production policy. The model developed here forecasts changes in the U.S. upland cotton farm price based on changes in U.S. cotton supply, the U.S. stocks-to-use ratio (S/U), China’s net imports as a share of world consumption, the foreign supply of cotton, and selected farm policy parameters.forecasting, cotton, price, demand, trade, structural change, farm programs., Agricultural and Food Policy, Agricultural Finance, Crop Production/Industries, Marketing, Production Economics,

Measuring the Potential Economic Impact of a Regional Agricultural Promotion Campaign: The Case of South Carolina

This study evaluated the impact of the South Carolina (SC) agricultural promotion campaign after its first season. Analysis of the survey data revealed that consumer demand for state grown produce has increased by 3.4% which could result in an increase in producer surplus of $2.9 million. Since the SC Department of Agriculture invested$500,000 in the promotion program in 2007, this figure indicates a benefit-cost ratio of 5.8.Demand for local products, state branding and promotion programs, contingent valuation, equilibrium displacement models, Agribusiness, Agricultural and Food Policy, Demand and Price Analysis,

To Fund or Not to Fund: Assessment of the Potential Impact of a Regional Promotion Campaign

This paper develops a framework for assessing the potential economic impact of a regional promotion campaign combining contingent valuation methods with a partial displacement equilibrium model. The proposed approach is applied to the evaluation of the potential economic impact of the locally grown campaign in South Carolina. Results reveal that the first season of the promotion campaign increased consumer willingness to pay for produce by 3.4%. The change in consumer preferences and the corresponding shift in demand increased producer surplus by $3.09 million. This economic benefit, combined with the 2007 promotion campaign investment, resulted in a benefit-cost ratio of 6.18.contingent valuation, economic impact, equilibrium displacement model, regional promotion campaign, Consumer/Household Economics,

Quantile Regression Methods of Estimating Confidence Intervals for WASDE Price Forecasts

This paper explores the use of quantile regression for estimation of empirical confidence limits for WASDE forecasts of corn, soybean, and wheat prices. Quantile regressions for corn, soybean, and wheat forecast errors over 1980/81 through 2006/07 were specified as a function of forecast lead time. Estimated coefficients were used to calculate forecast intervals for 2007/08. The quantile regression approach to calculating forecast intervals was evaluated based on out-of-sample performance. The accuracy of the empirical confidence intervals was not statistically different from the target level about 87% of the time prior to harvest and 91% of the time after harvest.Demand and Price Analysis,

What Can we Learn from our Mistakes? Evaluating the Benefits of Correcting Inefficiencies in USDA Cotton Forecasts.

This study investigated the magnitude of forecast improvements resulting from correction of inefficiencies in USDA cotton forecasts over 1999/00 to 2008/09 marketing years. The aspects of forecast performance included in this study were 1) bias and trends in bias, 2) correlation between forecast error and forecast level, 3) autocorrelation in forecast errors, 4) correlation in forecast revisions. Overall the results of this study demonstrated that some corrections of forecast inefficiencies, such as correction of correlation of error with forecast levels and correlation of error with previous year’s error resulted in consistent improvement of USDA cotton forecasts, while correction for correlation in forecast revisions did not benefit the forecasts. Correction for bias yielded mixed results likely because USDA has already been applying those corrections to some of the categories and thus our analysis resulted in over-correcting. The framework developed in this study can be used by USDA and other agencies to monitor and improve the performance of their forecasts.Commodity, Forecast evaluation, Fixed-event forecasts, Government forecasting, Forecast improvement, Agribusiness, Demand and Price Analysis, E37, E3, Q13,

Is there a "Right" Time to Buy Options Pre-Harvest?

This study analyses the variables that affect the option premium levels in an attempt to identify a period in time that would be considered "preferred" for the purchase of a December put option contract for corn and cotton. The daily futures and options data from January 1990 to October 2005 revealed that average prices of December cotton and corn futures tended to be higher in the month of March. The early months of the year also demonstrated low implied volatility levels while offering larger time to maturity. The analysis suggests that March may be a preferred time to purchase December cotton and corn put options.Marketing,

Quantile Regression Estimates of Confidence Intervals for WASDE Price Forecasts

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.commodity, evaluating forecasts, government forecasting, judgmental forecasting, prediction intervals, price forecasting, Crop Production/Industries, Demand and Price Analysis,

Accuracy of Implied Volatility Approximations Using "Nearest-to-the-Money" Option Premiums

Implied volatility is a useful bit of information for futures and options hedgers and speculators. However, extraction of implied volatility from Black-Scholes (BS) option pricing model requires a numeric search. Since 1988, there have been numerous simplifying modifications to the BS formula proposed and presented in the applied economics and finance literature to allow approximation of implied volatility directly. This study identifies and tests these simplification methods for accuracy for call only and put-call average elicitation of an implied volatility estimate. Results show that accuracy varies by method and whether call only or put-call average approaches are applied.Marketing,

The Impact of Situation and Outlook Information in Corn and Soybean Futures Markets: Evidence from WASDE Reports

The purpose of this study was to examine the impact of situation and outlook information from World Agricultural Supply and Demand Estimates (WASDE) in corn and soybean futures markets over the period 1985 to 2006. Results indicate that WASDE reports containing National Agricultural Statistics Service (NASS) crop production estimates and other domestic and international situation and outlook information have the largest impact; causing return variance on report sessions to be 7.38 times greater than normal return variance in corn futures and 6.87 times greater than normal return variance in soybean futures. WASDE reports limited to international situation information and domestic and international outlook information have a smaller impact. The results show that the impact of WASDE reports has increased over time.corn, market impact, outlook, situation, soybeans, WASDE, Agribusiness, Crop Production/Industries, Financial Economics, Q100, Q110, Q130,