Evaluation of USDA Interval Forecasts of Corn and Soybean Prices

USDA World Agricultural Supply and Demand Estimates (WASDE) price forecasts are published as an interval, but are typically analyzed as point estimates. Thus, all information about uncertainty imbedded in the forecast is ignored. The purpose of this article is to evaluate the accuracy of WASDE corn and soybean price forecasts using methodology suitable for testing judgmental interval forecasts. Accuracy tests suggest that WASDE forecasts are not calibrated at the 95% confidence level for both commodities and generally not calibrated for corn, but calibrated for soybeans, at the implied confidence level elicited from the survey of forecast providers.     

Improving the accuracy of outlook price forecasts

This study investigates the predictive ability of outlook hog price forecasts released by Iowa State University relative to alternative time‐series and market forecasts. Under root mean squared error (RMSE), the futures market forecast is most accurate at the first and second horizon but less accurate than Iowa outlook and the other forecast methods at the third horizon. In terms of the individual time‐series models, some vector autoregressions (VARs) and Bayesian VARs flexible in specification and estimation and model averaging tend to perform better than Iowa outlook forecasts. Evidence from encompassing tests, more stringent tests of forecast performance, indicates that many price forecasts can add incremental information to the Iowa forecast. Simple combinations of these models and outlook forecasts are able to reduce forecast errors by economically significant levels. Overall, the results indicate that it is possible to provide more accurate forecasts than Iowa outlook at every horizon.     

Out‐of‐sample testing price discovery in commodity markets: the case of soybeans

Price discovery, a central function of futures markets, has been usually tested in‐sample by studying the common stochastic trend between spot and futures prices. Instead, to evaluate futures as anticipatory prices, we develop a forecast approach to out‐of‐sample test price discovery in a multivariate framework. We apply it to the soybeans market. Results indicate futures prices as the best available “predictors” of future spot prices, although this finding holds only on average and for certain periods, other models show forecasting gains.     

How financial investment distorts food prices: evidence from U.S. grain markets

Convergence between commodity futures prices and the underlying physical assets at each contract's expiration date is a pivotal condition for the market's functioning. Between 2005 and 2010, convergence failed for several U.S. grain markets. This article presents a price pressure‐augmented commodity storage model that links the scale of nonconvergence to financial investment channeled through indices, which are traded in commodity futures markets. The model is empirically tested, using Markov regime‐switching regression analysis. Regression results strongly support the model's predicted link between index investment and the extent of nonconvergence for three grains traded at the Chicago Board of Trade: wheat, corn, and soybeans.     

The use of organized commodity markets to manage food import price instability and risk

The article explores the possibility of insuring the price risks of wheat and maize imports of low‐income food‐deficit countries (LIFDCs). Optimal strategies for an importing agent, who hedges with futures and options are derived, based on the objective of minimizing the unpredictability of import bills. Ex post simulations for a set of LIFDCs are run on wheat and maize imports hedged with futures and options in the Chicago Board of Trade, to explore the extent to which hedging reduces the unpredictability in import bills. Simulations encompass both periods of normal price behavior, as well as the period of global upheaval that occurred in 2007 and 2008. Results show that hedging with futures alone affords agents considerable opportunities for reducing import cost unpredictability, and the same holds with options, albeit, to a lesser extent. However, during the recent price spike of 2007–2008, hedging with options would have increased the unpredictability of some countries’ maize import bills, due to the combination of erratic import patterns and pronounced market uncertainty.     

The Term Structure of Implied Forward Volatility: Recovery and Informational Content in the Corn Options Market

Using a flexible method, we develop the term structure of volatility implied by corn futures options with differing maturities, and evaluate its ability to predict subsequent realized price volatility. The implied forward volatilities anticipate realized volatility well. For the nearby interval, the implied forward volatilities provide unbiased forecasts, and are superior to forecasts based on historical volatilities. For more distant intervals, early-year options predict the direction and magnitude of future volatility changes about as well as a three-year moving average and better than a naïve forecast. However, later-year options display less forecast power in part due to reduced trading activity.     

How much of commodity price behavior can a rational expectations storage model explain?

A rational expectations competitive storage model was applied to the U.S. corn market, to assess the aptness of this framework in explaining monthly price behavior in an actual commodity market. Relative to previous models, extensive realism was added to the model, in terms of how production activities and storage costs are specified. By modeling convenience yield, “backwardation” in prices between crop years did not depend on the unrealistic assumption of zero ending stocks. Our model generated cash prices that were distributed with positive skewness and kurtosis, and mean and variance that increased over the storage season, comparable to the persistence and the occasional spikes observed in commodity prices. Futures prices were generated as conditional expectations of cash prices at contract maturity, and the variances of futures prices exhibited realistic time–to‐maturity and seasonal patterns. Model realizations of cash and futures prices over many “years” were used to demonstrate the wide variety of price behaviors that could be observed in an efficient market with a similar market structure, implying that economic and policy implications drawn from short, historical samples of prices could be misleading.     

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.     

Insights from Anticipatory Prices

Contemporaneous observations on expected supply and on prices of post‐harvest futures contracts for corn are used to estimate expected demand relationships. These equations are used to estimate the prices of the post‐harvest contracts based on new supply estimates. Each estimate can be compared with a corresponding futures price, i.e. the market forecast. The differences help discern the market expectations about the expected demand for the new crop relative to historical experience, which can help support outlook analyses. We find that in recent years, a 100 million bushel change in the expected supply of corn results in about a 6 cent per bushel negative change in the price of December corn. The discussion also deepens understanding of the term ‘anticipatory prices’ as defined by Holbrook Working in his 1958 work.     

价格支持、政策调整及粮食市场波动

目的 为了估计价格支持政策对不同粮食品种期现货价格波动的直接影响,实证分析和比较了政策及其调整对粮食期现货价格波动实施效果的影响,为深化粮食价格形成机制改革提供一定的理论参考和实证支撑。方法 文章利用稻谷、小麦、玉米和大豆的现货与期货价格日数据,将政策以虚拟变量的形式引入GARCH模型实证分析最低收购价政策、临时收储政策及其调整对平抑粮食期现货市场波动的作用。结果 价格支持政策对粮食价格波动产生了显著影响,最低收购价政策能够明显降低稻谷和小麦现货市场的波动程度,但对期货市场波动的作用则相反;玉米和大豆临时收储政策的取消导致现货市场波动性提高,而对期货市场波动的影响存在差异。结论 价格支持政策具有降低价格波动的作用效果,政策调控效果与实施品种的国内供求及市场形势、国内外市场的联系程度密切相关,政策的完善还需关注对期货市场波动的影响。     

A speculative bubble in commodity futures prices? Cross‐sectional evidence

Recent accusations against speculators in general and long-only commodity index funds in particular include: increasing market volatility, distorting historical price relationships, and fueling a rapid increase and decrease in the level of commodity prices. Some researchers have argued that these market participants—through their impact on market prices—may have inadvertently prevented the efficient distribution of food aid to deserving groups. Certainly, this result—if substantiated—would counter the classical argument that speculators make prices more efficient and thus improve the economic efficiency of the food marketing system. Given the very important policy implications, it is crucial to develop a more thorough understanding of long-only index funds and their potential market impact. Here, we review the criticisms (and rebuttals) levied against (and for) commodity index funds in recent U.S. Congressional testimonies. Then, additional empirical evidence is added regarding cross-sectional market returns and the relative levels of long-only index fund participation in 12 commodity futures markets. The empirical results provide scant evidence that long-only index funds impact returns across commodity futures markets.     

Predictive performance of the World Bank's commodity price projections

The World Bank's commodity price projections are widely used for various planning purposes. Two aspects of the Bank's projections of relative prices are studied in this paper. The first is whether the forecasts make efficient use of the information available at the time the forecast is made. The second is whether the forecasts predict future prices with greater accuracy than alternative forecasting methods. These matters are studied by comparing the World Bank's past price projections with the actual prices that were subsequently observed. The results show that, overall, the World Bank forecasts do not pass either test. First, the World Bank forecasts are informationally inefficient. Prediction error (projection minus actual price) tends to be positively correlated with the projections themselves. Although the direction of future price movements tends to be correctly predicted, the magnitude of these movements tends to be overpredicted. Second, the World Bank forecasts do not perform well even compared with the simplest of alternative forecasting methods - the prediction of no change.     

Finding optimal price risk management instruments: the case of the Spanish potato sector

This article offers a comprehensive analysis of the problem of choosing between alternative market risk management instruments. We model farmers' behavior to optimize the certainty equivalent, formulated by a mean–variance model, by combining instruments with and without basis risk. Results are expressed as the demands for hedging with futures, forward contracts and insurance. Theoretical results are applied to a selection of Spanish producers of fresh potatoes, a sector that is exposed to significant market risks. Amsterdam's Euronext provides potato futures prices, and the recently launched revenue insurance in Spain provides the example for price insurance. Three conclusions summarize the article's main findings. First, we show that Spanish potato revenue insurance subsidies are a factor that determines the instrument rankings and choice. Second, the efficiency of insurance subsidies is generally low. Finally, the Amsterdam potato futures market does not provide a cost‐effective means to manage price risks for Spanish fresh potato growers.     

The role of the U.S. exchange-rate equity market volatility on agricultural exports and forecasts

This article estimates the U.S. state-level soybean export forecast until December 2024 using a seasonal autoregressive integrated moving average (SARIMA) model. We utilize the newly developed exchange-rate equity market volatility (EMV-EX) to improve model fit and the Dirichlet process mixture model (DPMM) to control for unobserved heterogeneity. Using monthly data from January 2004 to December 2020, the study shows that soybean exports for states without ports are underestimated at the expense of states with ports. The EMV-EX has a positive effect on soybean exports. The forecasts reveal no expected changes in the trends for soybean exports until December 2024. This study's results are useful to make and to implement more informed policy decisions for risk-mitigating strategies such as the market-facilitation program.     

Forecast Encompassing as the Necessary Condition to Reject Futures Market Efficiency: Fluid Milk Futures

The traditional necessary condition for futures market inefficiency is the existence of alternative forecasting methods that produce mean squared forecast errors smaller than the futures market. Here, a more exacting requirement for futures market efficiency is proposed—forecast encompassing. Using the procedure of Harvey and Newbold , multiple forecast encompassing is tested using Chicago Mercantile Exchange fluid milk futures. Time series models and USDA experts provide competing forecasts. Results suggest milk futures do not encompass the information contained in the USDA forecasts at a two-quarter horizon. While the competing forecasts generate positive revenues, it is unlikely that returns exceed transaction costs in this relatively new market.     

The economic impacts of technology and climate change: New evidence from U.S. corn yields

Over the past century, U.S. farmers have been offered a steady stream of new agricultural technologies, and more recently, experienced climate change. Because these two events have been occurring simultaneously, identifying their separate effects is difficult, and misimputation is easy. This article explicitly examines the economics of technical change and the interaction between weather and technology as revealed in a half century of panel data on U.S. Midwest rainfed state‐average corn yields. Observed yields reflect two components: yield potential and damage to the potential caused by weather and pests. Yield potential is modeled as a stochastic production frontier where nitrogen fertilization, public corn research, and introduction and adoption of biotech corn seeds impact yield potential and excess heat impacts nitrogen productivity. The yield‐damage/damage‐control function permits biotech corn plants to abate adverse effects of weather and pest events. Results include the following: nitrogen use, public corn research, and biotech seed‐corn adoption increase yield potential; soil moisture stress reduces yield potential, and excess heat severely reduces nitrogen productivity. Biotech corn plants abate yield damage caused by soil moisture stress but not excess heat.     

Measuring price discovery between nearby and deferred contracts in storable and nonstorable commodity futures markets

Using price discovery measures, including Putniņš’ (2013) information leadership share and intraday data, we quantify the proportional contribution of nearby and deferred contracts in price discovery in the corn and live cattle futures markets. On average, nearby contracts reflect information more quickly than deferred contracts in the corn market, but have a relatively less dominant role in the live cattle market. In both markets, the nearby contract loses dominance when its relative volume share dips below 50%, which typically occurs when the nearby is close to maturity. Regression results indicate that the share of price discovery is mainly related to trading volume and time to expiration in both markets. In the corn market, price discovery share between nearby and deferred contracts is also related to inverse carrying charges, crop year differences, USDA announcements, market crashes, and commodity index position rolls. Differences between corn and live cattle markets are consistent with differences in the contracts’ liquidity and commodity storability.     

Cotton market integration and the impact of China's new exchange rate regime

This article studies the integration of China's cotton market with the international market, especially the U.S. market. Investigating the futures prices from the Intercontinental Exchange (ICE) in the U.S. and the Zhengzhou Commodity Exchange (ZCE) in China with several time series models, we find that a long‐run cointegration relationship exists between these two series. The two markets share price transmissions, and based on results from an Autoregressive Conditional Heteroskedasticity (ARCH) model, we find their price volatilities are similar. We argue that China's recent exchange rate reform and its gradual liberalization in bilateral cotton trade since it joined World Trade Organization have had important impacts on these futures markets. Based on these findings, several important economic and policy implications are derived.     

Can the U.S. ethanol industry compete in the alternative fuels market?

The U.S. ethanol fuel industry has experienced preferential treatment from federal and state governments ever since the Energy Tax Act of 1978 exempted 10% ethanol/gasoline blend (gasohol) from the federal excise tax. Combined with a 54c/ /gal ethanol import tariff, this exemption was designed to provide incentives for the establishment and development of a U.S. ethanol industry. Despite these tax exemptions, until recently, the U.S. ethanol fuel industry was unable to expand from a limited regional market. Ethanol was dominated in the market by MTBE (methyl‐tertiary‐butyl ether). Only after MTBE was found to contaminate groundwater and consequently banned in many states did the demand for ethanol expand nationally. Limit pricing on the part of MTBE refiners is one hypothesis that may explain this lack of ethanol entry into the fuel‐additives market. As a test of this hypothesis, a structural vector autoregression (SVAR) model of the ethanol fuel market is developed. The results support the hypothesis of limit‐pricing behavior on the part of MTBE refiners, and suggest the U.S. corn‐based ethanol industry is vulnerable to limit‐price competition, which could recur. The dependence of the corn‐based ethanol price on supply determinants limits U.S. ethanol refiners' ability to price compete with sugar‐cane‐based ethanol refiners. Without federal support, U.S. ethanol refiners may find it difficult to compete with cheaper sugar‐cane‐refined ethanol, chiefly from Brazil.     

A quantile autoregression analysis of price volatility in agricultural markets

This paper investigates the dynamics of agricultural price volatility based on a quantile autoregression (QAR) model. The QAR model provides a flexible representation of the distribution of price and its dynamics. The approach is applied to U.S. wheat and corn markets over the period of 1980–2017. This period is of significant interest as it covers important changes in agricultural policy and increased reliance on markets. The price analysis is conducted conditional on stocks held in the previous period. We show how increasing previous stocks shift the price distribution to the left and decreases the odds of facing price spikes (by shifting down the upper tail of the price distribution). Our analysis also examines the effects of changing public stocks on prices. For both wheat and corn, this reflects changing agricultural policy, contrasting the 1980s (when public stocks were relatively high) with the post-2005 period (when public stocks became zero). We document how higher public stock ratio during the previous period did not lower the odds of facing price spikes. Applied to the wheat and corn markets, we also uncover evidence of local dynamic instability in the upper tail of the price distribution, suggesting that price instability becomes more pronounced when previous stocks are low.