NCCC-134
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The Long-term Effects of Meat Recalls on Futures Markets
Matt Houser and Jeffrey H. Dorfman
Year: 2017
 

Abstract

Over the past twenty years, there has been an increasing trend in the number of recalls. Despite increased safety control standards, foodborne disease outbreaks continue to impact the food supply. A common source of foodborne illness and fatal infection is beef from diseases such as E. coli 0157:H7, Listeria Monocytogenes, and Salmonella. Certain companies have even been bankrupted, unable to overcome the social costs and economic losses associated with recalls. We examine beef recalls over a twenty year period through an accumulated two-year index to see if there is a prolonged effect of recalls on current weekly cattle prices. We find that recalls act together, adversely impacting prices and decreasing farm-level revenue. The revenue drop is economically small; therefore, it is uncertain if beef recalls financially incentivize cattle producers to invest in food safety safeguards.

 
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Assessing the Accuracy of USDA’s Farm Income Forecasts: The Impact of ARMS
Todd H. Kuethe, Todd Hubbs, and Dwight R. Sanders
Year: 2017
 

Abstract

The USDA’s forecasts for net farm income are important inputs for businesses, legislators, economists, and other policy-makers. While the USDA has been providing forecasts for net farm income for over 50 years, they have not been rigorously analyzed in regards to bias and efficiency. Here, the USDA’s net farm income forecasts from 1975-2015 are evaluated along a number of dimensions. The results show that the USDA’s initial forecasts for net farm income are downward bias. This bias is corrected as the forecasts evolve through the year. Moreover, upward revisions tend to lead to negative forecast errors or overestimates. Consistent with that finding, there is a tendency for reversals in the forecasts. That is, upward revisions tend to be corrected in subsequent revisions. Despite these inefficiencies, the forecasts provide remarkably good directional guidance by predicting growth or contraction correctly over 80% of the time. There was no evidence that forecast accuracy improved through time or with the availability of ARMS data.

 
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Hedging Effectiveness of Fertilizer Swaps
William E. Maples, B. Wade Brorsen, and Xiaoli L. Etienne
Year: 2017
 

Abstract

One potential tool fertilizer dealers and producers have to protect themselves against fertilizer price risk is the fertilizer swaps market. Swaps usually settle using a floating variable price that is determined by an index of cash prices. This paper calculates hedge ratios and hedging effectiveness of urea and DAP (diammonium phosphate) swaps that settle using The Fertilizer Index with various spot price locations from the United States and internationally. Results show that urea and DAP swaps that settle using The Fertilizer Index perform poorly as a hedging tool over short time periods. As the hedging horizon increases, the hedging effectiveness of swaps improves.

 
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Time Series Modeling of Cash and Futures Commodity Prices
Joshua G. Maples and B. Wade Brorsen
Year: 2017
 

Abstract

Commodity prices exhibit differing levels of mean reversion and unit root tests are a standard part of the analysis of commodity price series. Changing underlying means are inherent in commodity prices and can create biased estimates if not correctly specified when performing unit root tests. Prominent financial models include terms for both mean reversion and unit roots but assume that mean reversion occurs gradually over time. Other models such as the popular error correction models require the researcher to determine if prices are either mean-reverting or follow a unit root process. We discuss the models commonly used for commodity prices and how their assumptions align with how commodity spot and futures prices actually behave. We argue for using panel unit root tests for futures prices as they allow for differing underlying means across futures contracts. Cash prices are difficult as none of the currently available models captures their likely stochastic process. Current models, however, can still be useful as close approximations.

 
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Examining Dynamically Changing Cattle Market Linkages with Inventory as Controlled Transitions
Yunhan Li, Wenying Li, and Jeffrey H. Dorfman
Year: 2017
 

Abstract

This article reports tests of price cointegration of cattle markets in the U.S. and proposes a simple procedure for incorporating a flexible transition function into the ECON smooth transition autoregressive (ECON-STAR) model to evaluate market dynamics over time. This model allows evaluating varying market integration as an exogeneous economic indicator changes throughout a specified time. Cattle are perishable, bulky and costly to transport. These characteristics make cattle markets easily segmented across regions. The empirical results show that these markets have been highly cointegrated when there exists excess supply. Following a sudden decrease in cattle inventory, the market pattern has become very regionally segmented.

 
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The Effects of Microstructure Noise on Realized Volatility in the Live Cattle Futures Market
Anabelle Couleau, Teresa Serra, and Philip Garcia
Year: 2017
 

Abstract

Recently, U.S. live cattle futures prices have experienced high levels volatility which has raised concerns about the impact of high frequency trading. This paper identifies the market microstructure noise present in high frequency data and its implications for realized volatility of returns in live cattle futures markets from 2011 to 2015. Short- and long-term components of volatility are identified using nonparametric and semi-parametric procedures. While market microstructure noise is found to increase realized volatility when the sampling frequency is below 4-minute time intervals, the particularly high volatility in live cattle markets in 2015 is found to be strongly driven by market fundamentals, affected by supply and demand shocks. Important policy implications from the results are drawn.

 
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The Cost of Forward Contracting in CIF NOLA Export Bid Market
Bradley Isbell, Andy M. McKenzie and B. Wade Brorsen
Year: 2017
 

Abstract

Price risk management in the grain industry is typically done by hedging with forward contracts and futures contracts. An additional important price discovery and risk management “paper market” also exists in the form of CIF NOLA basis bids, traded through brokers. These bids function similar to traditional forward contracts, however, like a futures market, firms can offset their forward contractual obligations by offsetting positions in a liquid off-exchange paper market. Analysis shows that this liquidity mostly removes the pricing bias commonly found in forward contracting in corn and soybeans, although a small bias still exists in wheat and especially sorghum.

 
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Is the Value of USDA Announcement Effects Declining over Time?
Jiahui Ying, Yu Chen and Jeffrey H. Dorfman
Year: 2017
 

Abstract

The value of USDA reports in commodity futures markets has been intensively researched, while whether such an effect is increasing or decreasing over time has rarely been answered. Given the fact that much more diverse information is available in today’s futures market, understanding trends in the impact of USDA announcement effects is crucial for market participants. This study measures how USDA reports’ announcement impact on market volatility has changed over time in both corn and soybean futures markets by adopting a new continuous approach with time-varying coefficients added to a model of the reports’ impact. The result shows that USDA reports are still informative and influential in commodity futures markets, with a generally increasing trend in the impact of announcement effects, while there are some reports whose impact show a declining potential.

 
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Evaluating Crop Forecast Accuracy for Corn and Soybeans in the United States, China, Brazil, and Argentina
Katie Cumming, Fabio Mattos and Xiaoli L. Etienne
Year: 2017
 

Abstract

Commodity prices are determined by the dynamics of supply and demand and they oscillate over time according to market participants’ price expectations, which are in return formed and updated based on new information available on the market. Previous studies have explored how the release of supply and demand news affect futures prices (e.g. McKenzie, 2008). This process of price discovery is crucial for various business decisions in the agricultural sector, such as production, marketing, and risk management.

 
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Performance of the Producer Accumulator in Corn and Soybean Commodity Markets
Chad Te Slaa, Lisa Elliott, Matthew Elliott, and Zhiguang Wang
Year: 2017
 

Abstract

This research quantifies risk reduction and performance of the producer accumulator contract in corn and soybean markets. To quantify performance, we use three alternative theoretical pricing models to estimate historical producer accumulator contract specifications in corn and soybean markets. We then compare the performance of the producer accumulator to eight alternative agricultural marketing strategy portfolios that are also used in new generation grain contracts. The performance measures we compare are: average bushel price that would be received by the producer, daily portfolio risk, and the Sharpe ratio. The period we examine performance was between 2008 and 2017. We investigate performance of the producer accumulator executed during each year, month, whether the contract was executed during the growing season or non-growing season, and beginning and following an uptrend, neutral trend, and downtrend ranging in length from 25 to 100-days. Specific to the producer accumulator, we also quantify bushels accumulated during the contract period. We find the average price the producer would expect to receive adopting an accumulator to slightly underperform the average price they would receive with a long futures portfolio in corn and slightly outperform long futures in soybeans. Nevertheless, the accumulator significantly reduces daily risk compared to the long futures portfolio. Indeed, producer accumulator portfolios produced average daily Sharpe ratios exceeding all other simulated risk management strategies in corn and soybeans on an average annual and average aggregate basis from 2008-2017. Consequently, the producer accumulator portfolio offered corn and soybean producers the best risk adjusted return to hedge production during this time-frame.

 
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Forecasting Hard Red Winter and Soft White Wheat Basis in Washington State
Wenxing Song and T. Randall Fortenbery
Year: 2017
 

Abstract

The objective of the study is rst to identify economic factors that in uence two speci c classes of wheat: hard red winter (HRW) and soft white (SWW) wheat, and develop models to improve the forecast performance of basis in Washington State. Earlier work has investigated basis behavior of some other classes of wheat, but none has examined soft white wheat. This class is unique because there is no direct futures contract-it is usually priced o the soft red wheat futures contract. The models we estimate include: 1) a simple moving average model to serve as a benchmark, 2) an econometric fundamental model, 3) an ARMA time series model, and 4) an ARMAX hybrid model. The econometric fundamental and ARMAX models include supply/demand factors suggested by economic theory and literature. We estimate all the models and then compare their forecast performance. Based on empirical results, we nd the best HRW model at the 4-month and 11-month forecast horizons is the econometric fundamental model, at the 5-month, is an ARMA(3,0,0) model, and the best model for the rest of the forecasts is an ARMAX (3,0,0). For SWW, the econometric fundamental model is the best overall. In addition, the ARMAX models perform better than the ARMA models in most cases, except SWW in Odessa, WA.

 
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Automation in the Hedge-Ratio Estimation Cottage Industry
Roger A. Dahlgran
Year: 2017
 

Abstract

Futures markets can be used to minimize a firm’s financial exposure to cash price fluctuations, but it’s costly to determine the futures position size that minimizes this risk. We present survey results that indicate that finding the risk-minimizing futures position requires 160 hours of skilled market analysts’ time spread over 60 days and costs between $15,000 and $25,000. This process can be automated so that optimal futures positions can be determined in minutes at a fraction of this cost. We introduce HedgeSmart, software that determines the optimal hedging strategy by combining user-supplied, business-specific data with the generally accepted price-risk minimization model and an up-to-date database containing more than 10 million records on commodity price movements. The user can incorporate his/her own historical commodity prices to insure that the analysis reflects specific location, grade, and pricing characteristics as appropriate to your firm. The time and cost savings that HedgeSmart achieves enables analysts to ask “what-if” questions, to explore alternative hedging approaches.

 
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Is Pit Closure Costly for Customers? A Case of Livestock Futures
Eleni Gousgounis and Esen Onur
Year: 2017
 

Abstract

Motivated by CME’s decision to close down most of the futures pits in July of 2015, we analyze the changes in the livestock futures market between 2014 and 2016. The livestock futures market, which had an active presence at the pit prior to the closure, has recently exhibited unprecedented price fluctuations. A simultaneous increase in the bid ask spread has raised concerns over the availability or liquidity in this market. The focus of our study is to analyze whether liquidity has changed for customer orders after the futures pit closed. In more detail, we track customer orders and evaluate their execution quality. We investigate whether execution costs for such trades have increased after the futures pits closed. In addition, we also examine whether customers have changed their trading behavior by placing more aggressive orders.

 
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Trade Impact in the Electronic Grain Futures Markets
Zhiguang Wang, Suchismita Mishra and Lisa Elliott
Year: 2017
 

Abstract

The rise of large/institutional traders in agricultural commodities calls for research on the analysis of market impact of trading. We aim to uncover the pattern and duration of market impact of trading in corn, soybeans and wheat during the period of 2008- 2015. Using the CME intraday electronic trade and quote data, we find support for square-root temporary impact of trading volume that lasts 10 minutes for corn and wheat, 16 minutes for soybeans. We also find evidence for post-trade permanent impact with a decaying effect in the form of -1/2 power of time. The permanent impact lasts 8 minutes for corn and wheat, 6 minutes for soybeans.

 
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Futures-Based Forecasts of U.S. Crop Prices
Jiafeng Zhu and Olga Isengildina-Massa
Year: 2017
 

Abstract

This study proposed two futures-based models for forecasting cash prices of corn, soybeans, wheat and cotton over the period 2000-2016. The difference model predicts changes in cash prices as a function of changes in futures prices. The regime model specifies different market regimes and models cash price levels based on observed futures prices in various regimes. The out-of-sample performance of both models was compared to the benchmark of a 5-year moving average over the 2013-2016 sub-period. Our results suggest that the regime model performed best for corn and soybeans. While, neither model beat the benchmark for wheat at the longer forecasted horizons, the difference model performed well at short forecast horizons (up to 5-months ahead). Both models performed better than the benchmark for cotton price forecasts, but they were not significantly different from each other.

 
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Corporate Hedging In Incomplete Markets: A Solution Under Price Transmission
Rui Luo and T. Randall Fortenbery
Year: 2017
 

Abstract

This paper provides dynamic minimum-variance hedges for firms in incomplete markets. Our hedges accounts for price transmission between the input and output prices, and thereby enable firms to minimize both input and output price fluctuations through tradable securities. Specifically, the model conditions on the direction and magnitude of price transaction between raw materials and products, as well as on the availability of futures contracts. A two-factor diffusion model is assumed for the underlying asset. The optimal hedges are the weighted average of the classic direct hedging and cross hedging ratios. We apply our results to the problem of a hypothetical jet fuel producer. Empirical results demonstrate the hedging effectiveness of this model.

 
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Are Futures Prices Good Price Forecasts? Nonlinearities in Efficiency and Risk Premiums in the Soybean Futures Market
Joshua Huang, Teresa Serra, Philip Garcia
Year: 2017
 

Abstract

Recent research has pointed to a reduction in predictive content in several agricultural futures markets. We investigate short-run forecast in the soybean futures market complex to identify predictive content and the sources of forecast errors. A non-parametric local linear regression framework is first applied to investigate biasness, and to guide the specification of parametric regime-switching models in which we perform statistical testing. To identify effects of risk premiums, we use a nonlinear realized volatility framework. Our non-parametric and parametric findings indicate nonlinearities in efficiency and risk premiums are present. Depending on the level of futures prices, thresholds or regimes of predictive performance exist. Evidence of market exuberance/pessimism emerges as many of the pricing errors occur at the extremes of the price distribution. Our research demonstrates that failure to account for these non-linear relationships can distort our understanding of market effectiveness. Finally, we show that the use of higher frequency data can be useful in identifying the presence and magnitude of risk premiums. This finding may make uncovering risk premiums in agricultural commodity markets more tractable in the future.

 
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Measuring Price Discovery between Nearby and Deferred Contracts in Storable and Non-Storable Commodity Futures Markets
Zhepeng Hu, Mindy Mallory, Teresa Serra, and Philip Garcia
Year: 2017
 

Abstract

Futures market contracts with varying maturities are traded concurrently and the speed at which they process information is of value in understanding the pricing discovery process. Using price discovery measures, including Putninš’ (2013) information leadership share and intraday data, we quantify the proportional contribution of price discovery between nearby and deferred contracts in the corn and live cattle futures markets. Price discovery is more systematic in the corn than in the live cattle market. On average, nearby contracts lead all deferred contracts in price discovery 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 occurs about 2-3 weeks before expiration in corn and 5-6 weeks before expiration in live cattle. Regression results indicate that the share of price discovery is most closely linked to trading volume but is also affected, to far less degree, by time to expiration, backwardation, USDA announcements and market crashes. The effects of these other factors vary between the markets which likely reflect the difference in storability as well as other market-related characteristics.

 
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