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The Rainfall Index Annual Forage Pilot Program as a Risk Management Tool for Cool Season Forage
Joshua G. Maples, B. Wade Brorsen, and Jon T. Biermacher
Year: 2015
 

Abstract

The recently implemented Rainfall Index Annual Forage pilot program aims to provide risk coverage for annual forage producers in select states through the use of area rainfall indices as a proxy for yield. This paper utilizes unique data from a long-term study of annual ryegrass production with rainfall recorded at the site to determine whether or not the use of rainfall indices provides adequate coverage for annual forage growers. The rainfall index is highly correlated with actual rainfall. However, it does not provide much yield loss risk protection for our specific data.

 
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Forecasting Urea Prices
Seon-Woong Kim and B. Wade Brorsen
Year: 2015
 

Abstract

Over the past decade the price of urea has been quite volatile, especially after 2008. The high price volatility and the relatively slow transportation in the urea fertilizer industry make production planning and inventory management difficult. In this study, we construct a urea price forecasting model and compare its performance with Fertilizer Week, a commercial forecast. To construct forecast models, autoregressive (AR), seasonal autoregressive (SAR), and autoregressive-generalized autoregressive conditional heteroskedasticity (ARGARCH) models with/without exogenous variables such as Henry Hub natural gas, Brent oil, and U.S. corn prices are used with various rolling windows. Autoregressive model with exogenous variable (ARX) using the window size of 48 months outperforms our other models. There is no statistical difference between ARX with the window size of 48 month and Fertilizer Week even though Fertilizer Week is better based on forecasting accuracy measures. The combination model using the two models is statistically better than Fertilizer Week alone.

 
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Extraordinary Monetary Policy Effects on Commodity Prices
AitbekAmatovand Jeffrey H. Dorfman
Year: 2015
 

Abstract

In the aftermath of the Great Recession, commodity prices have stabilized; however, the reasons are debatable. This paper concentrates on finding the relationship between Federal Reserve monetary policy and other macroeconomic indicators to both a broad commodity price index and an agricultural commodity price index by employing a vector error correction model. Excessive liquidity and the recent long period of ultra-low interest rates appear to have played a statistically significant role in affecting prices in the commodities markets. The responses of commodity prices to monetary policy that we estimate generally conform to earlier findings, but the sensitivity of the responses appears different in the face of the unprecedented scope of recent Fed activism.

 
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Using Local Information to Improve Short-run Corn Cash Price Forecasts
Xiaojie Xu and Walter N. Thurman
Year: 2015
 

Abstract

Using daily prices from 134 corn cash markets from seven Midwestern states, this study examines the increase in short-run cash price forecasting accuracy provided by augmenting futures prices with recent observations from other cash markets. We utilize a Granger-causality-based criterion to determine the structure of the augmented models, i.e. how far to look for potentially relevant forecast information. For about 65% of the markets, the model consisting of prices of the futures market, the specific cash market, and its nearby cash markets (M2) forecasts better than the one only incorporating prices of the futures market and the specific cash market (M1) for five-, ten-, and thirty-day ahead forecasts based on root mean squared error (RMSE). For short-run forecasts, RMSEs tend not to be significantly different for most of the cash markets investigated, suggesting that the forecast accuracy improvement from including nearby cash markets is only moderate. However, the expanded model (M2) tends to significantly outperform the bivariate model (M1) more often as the forecast horizon increases.

 
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Tests of the Difference between In-Sample and Post-Sample Hedging Effectiveness
Roger A. Dahlgran
Year: 2015
 

Abstract

Hedging effectiveness is proportional price-risk reduction achieved by hedging. Typically, hedging studies estimate hedging effectiveness for the sample period then use estimated hedge ratios to simulate hedging and estimate hedging effectiveness in a “post-sample” period. This paper derives the statistical properties of the sample-period effectiveness estimator and the statistical properties of the difference between the sample-period and the post-sample period estimators. We find that the bias associated with the sample-period estimator is negligible and that a difference between the sample estimator and the post-sample estimator ties directly to changes in the structural parameters of the hedge-ratio regression. We develop tests for structural change and demonstrate those tests with an empirical example.

 
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Will the Margin Protection Program for Dairy Producers Crowd Out Dairy Futures and Options?
Fanda Yang and Marin Bozic
Year: 2015
 

Abstract

The Margin Protection Program for Dairy Producers, created under the Agricultural Act of 2014, introduces a new margin insurance program that pays dairy producers when national incomeover- feed-cost margin declines below elected coverage level. A potential side effect of the program is that it may crowd out hedging using CME dairy futures and options. We analyze this issue under the assumption that hedging and Margin Protection Program are utilized by dairy producers as a protection against catastrophic margin risks. We model such behavior using safety-first preferences where farmers minimize hedge ratio subject to probabilistic constraint on revenue falling below a critical threshold level. We use Monte Carlo simulations and accounting data on dairy cost of production in two US regions to compare the crowding-out effect on representative producers in the upper and lower Midwest. We find that the magnitude of the crowding out depends on production efficiency, market risk exposure, and the timing of the Margin Protection Program sign-up.

 
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Pricing Corn Calendar Spread Options
Juheon Seok and B. Wade Brorsen
Year: 2015
 

Abstract

Previous studies provide pricing models of options on futures spreads. However, none fully reflect the economic reality that spreads can stay near full carry for long periods of time. A new option pricing model is derived that assumes convenience yield follows arithmetic Brownian motion that is truncated at zero. The new models as well as alternative models are tested by testing the truth of their distributional assumptions for calendar spreads and convenience yield with Chicago Board of Trade corn calendar spreads. Panel unit root tests fail to reject the null hypothesis of a unit root and thus support our assumption of arithmetic Brownian motion as opposed to a mean-reverting process as is assumed in much past research. The assumption that convenience yield follows a normal distribution truncated at zero is only approximate as the volatility of convenience yield never goes to zero. Estimated convenience yields can be negative, which is presumably due to measurement error. Option payoffs are estimated with the four different models and the relative performance of models is determined using bias and root mean squared error (RMSE). The new model outperforms three other models and that the other models overestimate actual payoffs. There is no significant difference in error variance for Hinz and Fehr, Poitras, and the new model, and the error variance of the new model is smaller than that of Gibson and Schwartz.

 
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Can Cattle Basis Forecasts Be Improved? A Bayesian Model Averaging Approach
Nicholas Payne and Berna Karali
Year: 2015
 

Abstract

Basis forecasts aid producers and consumers of agricultural commodities in price risk management. A simple historical moving average of nearby basis on a specific date is the most common forecast approach; however, in previous evaluations of forecast methods, the best prediction of basis has often been inconsistent. The best forecast also differs with respect to commodity and forecast horizon. Given this inconsistency, a Bayesian approach which addresses model uncertainty by combining forecasts from different models is taken. Various regression models are considered for combination, and simple moving averages are evaluated for comparison. We find that model performance differs by location and forecast horizon, but the average model typically performs favorably compared to regression models. However, except for very short-horizon forecasts, the simple moving averages have a lower out of sample forecast error than the regression models. We also examine using a basis series created using a specific month’s futures contract as opposed to the nearby contract and find that basis forecasts calculated this way have lower forecast errors in the month of the contract examined.

 
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Machine Learning for Semi-Strong Efficiency Test of Inter-Market Wheat Futures
Martial Phelippe- Guinvarc'h and Jean E. Cordier
Year: 2015
 
No Abstract Available

 
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Characterizing the Effect of USDA Report Announcements in the Winter Wheat Futures Market Using Realized Volatility
Gabriel D. Bunek and Joseph P. Janzen
Year: 2015
 

Abstract

The United States Department of Agriculture provides information about fundamental supply and demand conditions for major agricultural commodities. We consider whether USDA’s crop reports facilitate price consensus in the winter wheat futures market by testing the hypothesis that uncertainty, as measured by realized price volatility, is reduced following the release of USDA reports. This hypothesis was originally developed in studies using implied volatility and found significant decreases. We instead calculate realized daily and intraday volatility using transaction level data from Kansas City Board of Trade futures contracts. Dates on which USDA reports are released are compared to the ten days around the report. Exploiting the full granularity of data, intraminute volatilities are computed to test whether there are distributional differences between report and non-report days. All results suggest that realized volatility does not decrease following USDA wheat report releases but instead increases. Regression analysis shows this result is robust to the inclusion of a limited but relevant set of controls.

 
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The Effects of Brazilian Second (Winter) Corn Crop on Price Seasonality, Basis Behavior and Integration to International Market
Fabio Mattos and Rodrigo L. F. Silveira
Year: 2015
 

Abstract

The purpose of this study is to analyze the impact of the growth of Brazilian winter corn crop on spot price seasonality, basis patterns, and the integration to international market. A moving average method and regression analysis were used to test for seasonal variations, while econometric time-series methods tests were applied to verify the market integration. Results indicated that the expansion of the winter corn crop has changed the seasonality of prices and basis and increased the level of integration with the international market. crop on spot price seasonality, basis patterns, and the integration to international market. A moving average method and regression analysis were used to test for seasonal variations, while econometric time-series methods tests were applied to verify the market integration. Results indicated that the expansion of the winter corn crop has changed the seasonality of prices and basis and increased the level of integration with the international market.

 
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A New and Dynamic Look at Forecasting MPP Margin Price
Hernan A. Tejeda and Dillon M. Feuz
Year: 2015
 

Abstract

The recent Agricultural Act (Farm Bill) of 2014 considers a new insurance program for Dairy producers. The Margin Protection Program (MPP) accounts for the difference between the national average price of milk and feed, which includes corn, soybean meal and alfalfa. If this national dairy margin is below a threshold selected by the producer in their insurance contract, the producer receives an indemnity. A web-based tool available for access by dairy producers and/or dairy related stakeholders utilizes a method which forecasts this dairy margin, by making use of extensive futures and options market data. The method considers shocks to futures prices as the difference between a commodity’s expected price - given by its futures contract price at a certain date - and its eventual settled (terminal) price at expiration. These unexpected shocks are calculated monthly for each commodity according to their time-to-maturity, from one month ahead up to one year ahead (i.e. 12 different periods of price shocks). The inter-relationship (rank correlation) between the different ‘time-to-maturity’ shocks from these commodities is maintained when forecasting the futures (and subsequently cash) prices. However, these correlations are static - depending only on time-to-maturity of the different shocks - without incorporating additional information obtained from a growing crop season (e.g. method considers price deviates from March Corn futures expiring in September same as for September Corn futures expiring in March). Our method takes into account the arrival of new information during the farming season by incorporating time-varying (dynamic) correlations in the forecast method. In addition, we make use of dynamic copulas to model the joint time-varying interrelationship among these price shocks. Results obtained are of relative improvement in forecasting the actual margin, especially during the 1st three months. Discussion and remarks for future venues are provided

 
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Supply Shocks, Futures Prices, and Trader Positions
Joseph P. Janzen and Nicolas Merener
Year: 2015
 
No Abstract Available

 
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Relative Impact of StarLink and MIR162
Xue Han and Philip Garcia
Year: 2015
 

Abstract

Although various topics related to genetically modified (GM) technology have been studied worldwide, few studies have investigated the price impact of genetically modified food events. This paper contributes to the literature by examining the price effects of multiple genetically modified corn contamination events in the U.S. corn market. Using the relative price of substitutes method and the time-varying cointegration, we identify at least three possible structural breaks relevant to the genetically modified corn contamination events. Our empirical results suggest that MIR162 is the largest and longest GM-related break, but notice this break was initially influenced by changes in U.S. corn and sorghum supply, and EPA’s proposed reduction of the ethanol mandate. China’s rejection of U.S. corn and its substantial imports of U.S sorghum protracted the depression of U.S. corn relative to sorghum until late 2014. Commodity market participants, policy makers and researchers can apply the finding and approach of this paper for anticipating the price impacts of multiple shocks in the commodity markets.

 
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Contango and Backwardation as Predictors of Commodity Price Direction
Paul E. Peterson
Year: 2015
 

Abstract

This study examines whether term structure can be used as a predictor of commodity price direction. It uses daily prices for the S&P GSCI and each of the 24 underlying commodities from January 2007 through December 2013. During each day of the monthly roll period, one-month returns for each commodity are calculated and compared with the corresponding term structure. In nearly all cases, the relationship between returns and term structure is no different from that of a random process. Mean returns for each commodity under backwardation-only and contango-only are examined and in most cases are not significantly different from zero. A detailed examination of unleaded gasoline finds that returns on a long position are little affected by term structure, but heavily affected by the price trend in the underlying market.

 
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Forecasting Returns to Storage: The Role of Factors other than the Basis Strategy
Sanghyo Kim, Carl Zulauf and Matthew Roberts
Year: 2015
 

Abstract

Given the interest in the ability to forecast returns to storage and the incon- clusiveness of the performance of the basis strategy, especially for unhedged storage; this study examines whether other variables enhance the forecast of storage returns. Speci cally, the rate of harvest progress and the ratio of a demand for storage space relative to the supply of storage space are examined. The later variable has not been investigated by previous studies of the basis strategy. Using data for Illinois corn and soybeans over the 1988 through 2012 crop years and a xed e ect seemingly unrelated regression estimation, both variables are found to be signi cant in explaining observed returns to unhedged storage but not to hedged storage. Given the regression results, storage strate- gies based on harvest progress and ratio of demand to supply for storage space are constructed. These strategies do not improve the basis strategy's return and risk for hedged storage. In contrast, these strategies improve the return and risk performance of unhedged storage relative to routine unhedged stor- age. Moreover, net return for the alternative strategies for unhedged storage is higher than net return for the basis strategy for hedged storage but the latter has a lower risk than the former. This nding is the classic return-risk tradeo and provides an explanation for the common observation that storage is often unhedged, especially by farmers.

 
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Forecasting Commodity Price Volatility with Internet Search Activity
Arabinda Basistha, Alexander Kurov and Marketa Halova Wolfe
Year: 2015
 

Abstract

Commodity prices are volatile. Forecasting the volatility has been notoriously difficult. We propose using Internet search activity to forecast commodity futures price volatility. We show that Google search volume improves forecasts of volatility both in-sample and out-of-sample in all commodity categories (energy, metal and agriculture).

 
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Information Content of USDA Rice Reports and Price Reactions of Rice Futures
Jessica L. Darby and Andrew M. McKenzie
Year: 2015
 

Abstract

Rice is a predominant food staple in many regions of the world, and international rice markets play a vital role in ensuring the food security needs of developing countries. It is important to develop economic tools and market-based instruments to aid in the price discovery process, mitigate the effects of price instability, and make rice markets more responsive to the needs of both consumers and producers. The purpose of this study is to estimate the economic value of USDA supply and demand forecasts, specifically WASDE and NASS, with respect to rice futures markets. Two event study approaches are utilized in this study: (1) examine variability in returns on report-release days as compared to returns on pre- and post-report days, and (2) regress price reactions on changes in usage and production information. It is found that the USDA provides the rice futures markets with valuable information, and rice futures respond to the information in an economically consistent manner.

 
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Measuring and Explaining Skewness in Pricing Distributions Implied from Livestock Options
Andrew M. McKenzie, Michael R. Thomsen, and Michael K. Adjemian
Year: 2015
 
No Abstract Available

 
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Performance of 5-Year Olympic Moving Average in Forecasting U.S. Crop Year Revenue for Program Crops
Sanghyo Kim, Carl Zulauf, Matthew Roberts, and Kevin Cook
Year: 2015
 

Abstract

The last two farm bills have used moving averages and Olympic moving averages in computing revenue benchmarks and hence payments in the Average Crop Revenue Election (ACRE) program in the Food, Conservation, and Energy Act of 2008 and its more recent version, the Agricultural Risk Coverage (ARC) program in the Agricultural Act of 2014. Accurate revenue forecasting is important to farmers and agribusiness managers because of the variety of risks associated with farming including price and yield variability, which are often negatively correlated. This paper therefore assesses the performance of various speci cations of simple and Olympic moving averages in forecasting U.S. crop year revenue for the program crops of corn, soybeans, wheat, rice, and sorghum over the 1974 through 2013 crop years. In general, forecast error is found to be lower for the moving average than for the Olympic moving average technique. It was also generally lower for the technique of forecasting revenue directly than for forecasting separating the price and yield components of revenue. Last, forecast error was generally smaller for calculation windows smaller than the 5 years used as the underlying method by the ARC farm support program.

 
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Anticipatory Signals of Changes in Corn Demand
Leslie J. Verteramoand William G. Tomek
Year: 2015
 

Abstract

This paper analyzes changes in the expected demand for corn in the U.S., and it explores whether anticipatory signals of price jumps can be obtained from simple models. Two main objectives are considered. One is to estimate the relationship between the expected supply of corn and corresponding prices of futures contracts. We argue that such results can provide estimates of demand relationships and their shifts with the passage of time. Moreover, such analysis should allow us to test for possible changes in the structure of demand. A second, related objective is to demonstrate how such historical estimates can allow an analyst to appraise the futures markets’ quotes relative to forecasts from the historical model. We argue that the difference in the two prices may contain useful information.

 
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Accuracy-Informativeness Tradeoff for Interval Forecast Comparison
Olga Isengildina and Fabio Mattos
Year: 2015
 

Abstract

Price interval forecasts are analyzed in this study focusing on three main characteristics: coverage, error and informativeness. The tradeoff between accuracy and informativeness results from the fact that greater accuracy is achieved at the cost of lower informativeness and vice versa. The purpose of this paper is to evaluate user preferences for these characteristics using experimental methods. Contingent valuation methods were used to elicit user willingness to pay for forecasts with various characteristics. Estimation results demonstrate that coverag

 
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Bubbles, Froth, and Facts: What Evidence is there to Support the Masters Hypothesis?
Dwight R. Sanders and Scott H. Irwin
Year: 2015
 

Abstract

The Masters Hypothesis is the assertion that large investment inflows into long-only commodity index funds pushed prices far above fundamental value. In particular, the architect of the hypothesis—Michael Masters—suggests that long-only index funds were the cause of a massive increase in commodity prices that culminated in mid-2008. Since that time, there has been a veritable explosion in empirical research on commodity market bubbles and the Masters Hypothesis. In this research we carefully dissect the typology of this literature with particular care given to the distinction between financialization impacts and actual bubble impacts. After carefully defining the characteristics of a Masters-like bubble, simple empirical tests are conducted on the 12 agricultural markets included in the CFTC’s Supplemental Commitments of Traders data base. Price behavior consistent with the Masters Hypothesis is surprisingly difficult to find in the data. This is an important finding given the on-going policy debate and regulations proposed to limit speculative positions in these markets.

 
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Nearby and Deferred Quotes: What They Tell Us about Linkages and Adjustments to Information
Mindy Mallory, Philip Garcia, and Teresa Serra
Year: 2015
 

Abstract

Recently, the ‘Financialization’ of commodity futures markets, biofuel production, climate change and rising demand potentially have imposed profound shifts in the way commodity futures markets operate. This article examines commodity markets tick-by-tick and quote-by-quote to develop metrics on liquidity and transformation of information. The metrics are based on insights we combined from the sequential trading models on single securities, index futures based on a basket of securities, and special features of commodity futures markets. Correlation between quote revisions in nearby and deferred contracts measure information-based activity, and correlations between revisions of the time lagged nearby and deferred maturity measure the speed at which information is transmitted among the different futures maturities. Information based trading results in near perfect correlation between revisions to bids and offers in nearby and deferred contracts. Within one second information has been fully transmitted from nearby to deferred contracts.

 
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