NCCC-134
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Impacts of Non-Grade Quality Factors on North Dakota Origin Soybean Basis Values: A Panel Regression Analysis
David W. Bullock, William W. Wilson, and Ryan Thompson
Year: 2021
 

Abstract

The impact of protein and essential amino acid (cysteine, lysine, methionine, threonine, and tryptophan) content upon North Dakota origin basis values was examined using a panel dataset covering eight Crop Reporting Districts (CRDs) over ten marketing years (2009/10 through 2018/19). Fixed-effect panel regression models were estimated based upon marketing year and quarterly averages to capture seasonal effects related to the timing of quality report releases. Principal component analysis (PCA) was applied to the amino acid quality measurements as a data reduction technique and to correct for multicollinearity in the variables. The regression results indicated a statistically significant positive relationship between three essential amino acids (methionine, threonine, and tryptophan) and local basis values in the quarters corresponding with and just following the release of the annual quality reports. Protein content was found to have little to no effect upon local basis values when considered with the amino acid measurements.

 
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A Comprehensive Evaluation of Commodity Tracking Divergence Divergence
Colburn Hassman, Olga Isengildina-Massa, and Shamar Stewart
Year: 2021
 

Abstract

This paper investigates differences in returns between the ETF price, Net Asset Value, and Benchmark Asset Baskets for five popular futures-backed ETFs. We decompose tracking difference to examine the relative size of tracking differences attributable to managers versus the arbitrage process. Tracking differences attributable to managers is found to be significantly smaller than that attributable to the arbitrage process. We then test for average Tracking Differences using the Mincer-Zarnowitz Equation. We find evidence of bias in returns for multiple ETFs and demonstrate the usefulness of the decomposition. Furthermore, we investigate the dynamics of Tracking Error using a GARCH methodology. We find support that the volatility of the ETF effects Tracking Error but find no evidence that rolling futures contracts influences Tracking Error.

 
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Buying Time: The Effect of Market Facilitation Program Payments on the Supply of Grain Storage
Bryn Swearingen and Joseph Janzen
Year: 2021
 

Abstract

We estimate the impact that the Market Facilitation Program (MFP) payments had on farmers' willingness to store grain. Using a fixed effects model across multiple dimensions and state-level data on MFP, grain stocks, production, and export dependence, we address the role of the decrease in opportunity costs causing an increase in the willingness to store of farmers. Our analysis finds that MFP payments had a significant impact on grain storage by US farmers. In states with relatively higher payments at the marginal 10% increase in payments a 1.28% increase in on-farm inventories will occur holding all else constant. This explains that policies that increase access to financial capital can cause small increases to grain inventories.

 
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A Multivariate Quantile Analysis of Price Transmission in the Soybean Complex
Yao Yang and Berna Karali
Year: 2021
 

Abstract

Asymmetric price transmission has been an important question for understanding the price relationship among input and output markets in a supply chain. This study investigates asymmetric price transmission in the U.S. soybean complex by using a vector autoregressive quantile model. We use daily returns of the soybean, soybean meal, and soybean oil futures contracts traded at the Chicago Board of Trade (CBOT). To better illustrate dynamics of the own- and cross-market effects, we consider both lower and upper tails and the median of price distributions. Our results indicate existence of asymmetric price transmission varying by the quantile. In addition, quantile impulse response analysis shows that soybean returns at a low level are more severely affected by the shocks from the soybean meal market, while those at a high level are more affected by shocks generating from the soybean oil market.

 
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How Do Changes in Market Fundamentals Affect Hedging in US Live Cattle Markets?
Walter Ac-Pangan and Brian K. Coffey
Year: 2021
 

Abstract

An increase in basis variability complicates hedging price risk management and causes hedging effectiveness to decrease. This is one reason that volatility in US live cattle basis has raised concerns over the last decade. The purpose of this analysis is to evaluate how changes in market fundamentals and price momentum impact live cattle hedging effectiveness and how the impacts vary regionally. This study used weekly data series to estimate regional hedonic models where the dependent variable was Basis Prediction Error, which serves as a hedging effectiveness measure, and the independent variables represent the shifts in the market fundamentals. The results suggest that a positive change in factors such as the thinness of the negotiated market and cost of gain will increase the Basis Prediction Errors. In contrast, variables such as the average weight per head of the live cattle marketed and delivery costs have an opposite influence. The analysis of the monetary impact of explanatory variables shocks showed that changes in the costs of gains have a larger monetary impact on the basis for Kansas. For Nebraska are the changes in delivery costs, and the Iowa-Minnesota region is more sensitive to changes in the current premium for high-quality beef.

 
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Identifying the Purpose and Success of Dairy Futures Contracts: Are Class III and Cheese Futures Contracts Serving Distinct Markets?
Hernan A. Tejeda, Andres Trujillo-Barrera and T. Randall Fortenbery
Year: 2021
 

Abstract

Weekly cash cheese prices are highly correlated with weekly average cheese futures prices. In addition, cash cheese prices exhibit a high degree of correlation with weekly average Class III milk futures. Lastly, cheese futures and Class III milk futures are highly correlated. Based on trading volume, the Class III milk futures market is more than five times larger than the cheese futures market, and yet both are quite thin compared to derivative markets for grains and cattle. Moreover, less than two percent of U.S. cheese production between 2009 and 2018 was traded in the cheese futures market. Given the rather small number of trades in both Class III and cheese futures, and their high level of price correlation, one wonders whether the separate dairy contracts are serving unique and distinct markets. Would it be (more) beneficial if only one of the contracts existed? If the markets are effectively redundant, the elimination of one could increase liquidity in the other, and potentially reduce overall price volatility. The objective of this paper is to investigate whether its possible market performance could be improved through the trading of a single dairy futures contract, and develop a baseline for evaluating the potential economic and financial benefits that would result.

 
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Correlations Go to One in a Crisis: Did the COVID 19 Market Crash Bring Cattle Futures and Equities Together?
Eli Mefford and Mindy Mallory
Year: 2021
 

Abstract

We investigate the impacts of Covid-19 on cattle futures markets during the first half of 2020. This study focuses on cattle futures response to the equities crash in March of 2020 and the Covid-linked production delays at beef packing plants. We observe that the initial declines in cattle futures began prior to the onset of beef packing plant shutdowns. Analysis comparing live cattle contracts, feeder cattle contracts, and corn contracts to the E-Mini S&P 500 futures contract finds evidence that the S&P 500 had a significant impact on cattle prices during March of 2020. These results are an example of increased cross asset correlation during periods of financial distress.

 
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The Role of Storage in Commodity Markets: Indirect Inference Based on Grains Data
Christophe Gouel and Nicolas Legrand
Year: 2021
 

Abstract

This paper develops and estimates a rational expectations storage model with rich dynamic features. The model incorporates elastic supply, long-run demand and cost trends, and four structural shocks. It is estimated by indirect inference using a linear supply and demand model as an auxiliary model. This approach deviates from the common practice of estimating storage models only on prices and allows all parameters to be estimated. The estimation is carried out on a market representing the caloric aggregate of four basic staples: maize, rice, soybean, and wheat. The results show that our extended storage model is consistent with most of the moments in the data, including the high price autocorrelation which is the subject of longstanding debate. However, it fails to replicate the strength of the negative correlation between consumption and price.

 
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Evaluating USDA's Baseline Projections
Siddhartha Bora, Ani L. Katchova and Todd Kuethe
Year: 2021
 

Abstract

Agricultural baselines play an important role in shaping agricultural policy, yet there are few studies evaluating these projections. This study evaluates the accuracy and informativeness of two widely used baselines for the US farm sector published by the United States Department of Agriculture (USDA) and the Food and Agricultural Policy Research Institute (FAPRI). First, we examine the average percent errors of the projections and perform tests of bias. Second, we use a novel testing framework based on the encompassing principle to test the predictive content of the projections for each horizon (year), determining the longest informative projection horizon. Third, we compare the USDA and FAPRI baseline projections using a multi-horizon framework that considers all projection horizons together. We find that prediction error and bias increase with the horizon's length. The predictive content of the baselines projections for most variables diminishes after 4-5 years from the current year. The multi-horizon comparison suggests that neither the USDA nor the FAPRI projection has uniform or average superior predictive ability over the other projection. Our findings are useful for the agencies producing these baselines, and policymakers, agricultural businesses, and other stakeholders who use them.

 
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The Impact of Futures Contract Storage Rate Policy on Convergence Expectations in Domestic Commodity Markets
Alankrita Goswami, Michael K. Adjemian and Berna Karali
Year: 2021
 

Abstract

Grain futures contracts that permit physical delivery do so through an exchange of delivery instruments. Because delivery instruments can be held indefinitely, extant research shows that futures contracts that assign inflexible and low storage rates relative to the market price of storage facilitate basis nonconvergence. In response to the notable episode of non-convergence in the mid- to late-2000s, the Chicago Mercantile Exchange (CME) introduced variable storage rate (VSR) policies in the soft red winter (SRW) wheat and hard red winter wheat markets. The VSR mechanism functions by adjusting the storage rate to the price spread between sequential futures contract deliveries in the period right before the expiration of the nearby contract. In contrast, CME did not introduce a VSR to corn and soybean markets but chose to increase their fixed storage fees in 2008 and later in 2020. We study convergence performance for each of these markets from 2006-2020 and use time series techniques to show that flexible storage fee policies like the VSR reduce the magnitude and therefore the expected duration of nonconvergence in wheat markets. On the other hand, we do not find evidence that CME's higher fixed storage rates likewise reduce the expected duration of nonconvergence episodes in corn and soybeans markets - although perhaps not enough time has passed to evaluate the effectiveness of the most recent changes - or that index trader activity causes basis nonconvergence.

 
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Decomposing USDA Ending Stocks Forecast Errors
Raghav Goyal, Michael K. Adjemian, Joseph Glauber & Seth Meyer
Year: 2021
 

Abstract

The U.S. Department of Agriculture (USDA) publishes monthly Ending Stocks projections, providing an estimate of the end-of-marketing-year inventory of a particular commodity, which effectively summarizes supply and demand outlook. By comparing USDA's projections of balance sheet variables against their realized values from marketing years 1992/3 to 2019/20, we decompose ending stocks forecast errors into errors of the other supply and demand components. We apply a decision-tree-based ensemble Machine Learning (ML) algorithm, Extreme Gradient Boost Tree (EGBT), that uses a gradient boosting framework and is robust to multicollinearity. Our results indicate export and production misses to be the major contributors to ending stocks projection errors. Because foreign imports are likely tied to foreign production deficits, we likewise investigate how U.S. export errors are linked to USDA's foreign production and export forecast misses, country-by-country, and show that misses on production and export levels in China, Mexico, Brazil, and Europe cost USDA the most.

 
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Forecasting Winter Wheat Basis with Soil Moisture Measurements
Noah Miller, Mykel Taylor, Ignacio Ciampitti, and Jesse Tack
Year: 2021
 

Abstract

Recent literature on basis forecasting has attempted to improve upon naive forecasts by incorporating current marketing year information (in the form of basis deviation from historical levels). Given that basis is determined at each grain elevator location, inclusion of proxies for current, local supply conditions could alternatively improve upon naive forecasts. Large weather datasets have been effectively utilized in the agronomy literature to predict harvest yields in real time, however they have, to the best of our knowledge, not been incorporated into any previous basis price forecasting research. Here we match 858 weekly elevator-level, winter wheat price observations (spread over eight states and spanning 14 years) to gridded soil moisture readings (at a 0.25 x 0.25 spatial resolution) obtained from the European Space Agency and forecast winter wheat harvest basis from each weekly vantage point within the growing season. Our results indicate that inclusion of soil moisture into basis forecasts can substantially improve harvest basis forecasts for forecasts made in the middle and final part of the growing season; this result is improved upon further by disaggregating the data and forecasting at the sub-state, regional level.

 
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Canary in the Coal Mine: COVID-19 and Soybean Futures Market Liquidity
Kun Peng, Zhepeng Hu, Michel A. Robe, and Michael K. Adjemian
Year: 2021
 

Abstract

We document the impact of the early stages of the COVID-19 pandemic on liquidity in U.S. agricultural markets. Notably, we show that soybean futures-market depth collapses weeks before the U.S. financial markets' crash of March 2020. Soybean futures liquidity is affected the earliest, the most, and the longest. Soybean depth drops by half for outright futures and by over 90 percent for calendar spreads, and soybean bid-ask spreads increase significantly. This liquidity pullback starts on the night of February 12 to 13, 2020 - a full two weeks before (i) liquidity evaporates in U.S. bond and equity markets and (ii) soybean prices start to fall sharply. The start of the soybean liquidity pullback coincides with overnight news of bleak COVID-19 developments in China (a dominant source of world demand for oilseeds). Following a series of emergency interventions by the U.S. Federal Reserve in March and April 2020, liquidity recovers in the soybean outright futures market - but depth remains abnormally low for calendar spreads. These patterns cannot be explained by other factors, such as seasonalities or changes in soybean futures trading volume and price volatility: the COVID-19 shock was novel, and it destroyed soybean-market liquidity in a way that foretold financial-market developments two weeks later. In contrast to soybeans, we find little evidence of a drop in corn or wheat futures liquidity until U.S. financial and crude oil markets sink in early March. Soybeans were truly the canary in the coal mine.

 
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Maximum Order Size and Agricultural Futures Market Quality: Evidence from a Natural Experiment
Kun Peng, Zhepeng Hu and Michel A. Robe
Year: 2021
 

Abstract

We exploit an exchange-mandated increase of the maximum order size, in the U.S. corn calendar spread market, in order to investigate the connection between exogenous constraints on order placement and execution, volatility, and liquidity. We show that the old maximum of 2,500 contracts was binding, and that demand exists for placing and executing much larger orders. The limit-order book depth (at the best bid and ask) increases dramatically after the exchange quadruples the maximum order size to 10,000 contracts. Intraday realized volatility is not statistically significantly different before and after the rule change. Amid increased market depth and stable volatility, we document that quoted and effective spreads both narrow significantly and that the price impact of large trades is smaller. In sum, market quality is higher after the maximum order size change.

 
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Do Extreme CIT Position Levels Have Market Impacts in Grain Futures Markets?
Jiarui Li, Scott H. Irwin, and Xiaoli L. Etienne
Year: 2021
 

Abstract

The "Masters Hypothesis" argues that the growing buying pressure from commodity index funds since 2000 drove up food and energy prices. A group of studies use linear Granger causality tests to examine the relationship between the speculative pressure and futures prices in agricultural futures markets. Some of them find little evidence to support the Masters Hypothesis, however, some of them find significant statistical links between the two series. We add the results from the quantile Granger causality test and the newly developed quantile dependence measure called cross-quantilogram to the standard linear Granger causality tests. Our findings suggest from 2004 to 2019, both extreme quantiles and the mean frameworks do not provide any supporting evidence for the Masters Hypothesis.

 
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