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| Managing Risk in Ethanol Processing Using Formula Pricing Contracts
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| David W. Bullock and William W. Wilson |
| Year: 2019 |
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Abstract
Manufacturers of ethanol face considerable pricing risk from both an input (corn and natural gas) and output (ethanol, distillers dried grains, and corn oil) in addition to the fluctuating value of the ethanol renewable identification numbers (D6 RINs) attached to each gallon of ethanol produced. Additionally, ethanol plants face technical risks related to their physical plant extraction rates for ethanol, DDGs, and corn oil along with their efficiency in using natural gas (or an alternative heat source). The purpose of this study is to examine the risk characteristics of a fixed margin, formula pricing contract applied to the ethanol industry using Monte Carlo simulation and sensitivity analysis. The margin model is set up for a typical South Dakota dry mill plant that has corn oil extraction capabilities in addition to dry DDGs. The results indicate that there are benefits to both the buyer and seller from utilizing the proposed contract. Under the average pricing scenario, the buyer can expect to pay a marginally lower mean ethanol price with a slightly lower probability of paying a high price and a slightly higher probability of paying a low price when compared to paying the spot ethanol price at delivery. At the mean, the buyer could feasibly save approximately 30 cents per gallon (on an average $1.43 delivery price) through perfect timing on setting the price components. For the seller, the gain from the contract is primarily due to a substantial reduction in margin volatility and better 5% value-at-risk (VaR) values when compared to the delivery benchmark under all three buyer pricing scenarios. The ethanol seller can also achieve gains in margin through increased ethanol extraction efficiency with an approximate 2% gain in margin for each 1% increase in the extraction rate.
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| Factors Influencing the Gulf and Pacific Northwest (PNW) Soybean Export Basis:
An Exploratory Statistical Analysis
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| David W. Bullock and William W. Wilson |
| Year: 2019 |
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Abstract
Growth in the export marketing of soybeans has drawn attention to the basis volatility in these market channels. Indeed, there has been greater growth in soybean exports compared to other commodities and this is due in part to the growth of exports to China. Concurrently, there has been substantial volatility in the basis at the primary U.S. export locations: the U.S. Gulf and the Pacific Northwest (PNW). The purpose of this study is to examine the impact of supply/demand, export competition and logistical variables on both the average level and seasonality (analog year) of U.S. export basis values for the 2004/05 through 2015/16 marketing years (September through August for U.S. soybeans). The results indicate that the average market year level of the basis is primarily influenced by export competition from Brazil and export demand - particularly from China; however, domestic demand (soybean crush) also has some influence. Rail transportation costs to both the Gulf and PNW have an influence on the basis level; however, barge and ocean freight rates appear to not have a significant influence on the level of the basis. Application of agglomerative hierarchal clustering resulted in the identification of 5 and 4 distinct analogs (over the 12 marketing years in the dataset) for the Gulf and PNW respectively. Application of the two-sample mean difference tests (Lebart, Morineau and Piron 2000) to the analogs indicate that the seasonal analog of the export basis is more heavily influenced by internal logistical conditions (late railcar placement and secondary railcar values), pace of farmer marketings, transportation cost differentials (between ports), and individual port export activity (ships in port and export inspections) rather than international and domestic demand.
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| Corn-Crush Hedging – Does Location Matter?
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| Roger A. Dahlgran and Rajat Gupta |
| Year: 2019 |
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Abstract
At the end of 2018, 200 ethanol refineries were operating in the U.S. and processing nearly 40
percent of U.S. corn production. These refineries are widely dispersed and the typical ethanol
refining firm operates several plants. Hedging is widely used as a price risk management
strategy. The dispersion of a given firm's plants leads us to ask the question posed by the title of
this paper – should the plant's location be considered in constructing a hedging program?
We tested this notion by drawing a sample from the plants operated by a multi-state/multi-plant
ethanol refiner. We interviewed plant managers to get information about input-output
coefficients, inventory turnover, plant efficiency comparisons, and hedging horizons. This
information informed our modelling. To test our premise, we sought plant location prices for
corn, natural gas, ethanol, dried distiller gran, and distiller corn oil. Local corn prices were
obtained but we had to use proxies and state averages for the other prices.
Standard hedging methodology was applied as we examined two hedging strategies: (a) hedging
the crush margin, and (b) hedging individual commodity transactions then combining these
hedges according to the input-output coefficients to hedge the crushing margin. Approach (b)
produced better results but the data limitations hindered testing our main hypothesis. In addition
to variations in hedge ratios for each location, we also discovered that (a) storage periods for
input and output inventories are short (1 to 2 weeks), (b) input-output coefficient variability
across plants creates opportunities for location specific hedging strategies, and © previous
studies that are based on aggregated cash prices likely overstate the effectiveness of local
hedging strategies.
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| Basis Forecasting Performance of Composite
Models: An Application to Corn and Soybean
Markets
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| Kexin Ding and Berna Karali |
| Year: 2019 |
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Abstract
Numerous studies have examined the performance of different models on basis forecasting while
none of them has compared relative performance of composite models. In order to further
improve basis forecasting accuracy, the crux of hedge management strategies, we investigate
basis forecasting performance of selected composite models, as well as various individual
models. Empirical results based on weekly futures and cash prices for major North Carolina
corn and soybean markets indicate that composite models have more stable and better
performance in forecasting basis compared to individual models' forecasts. The informationtheoretic
forecast combination method is found to be superior among the composite models
considered.
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| The Delaying Effect of Storage on Investment:
Evidence from The Crude Oil Sector
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| Assia Elgouacem and Nicolas Legrand |
| Year: 2019 |
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Abstract
Our paper provides a theoretical framework able to represent with accuracy a consistent relationship
between fixed capital investment, storage and the term structure of prices in a storable commodity
market. It aims at understanding the interaction of storage capacity with irreversible investment
decisions in mediating investment and commodity price dynamics. The results show that the presence
of storage, while smoothing the spot price tends also to channel volatility into the future, thereby
raising the options value of waiting and eventually delaying and making lumpier the investment
in fixed capital. The time-varying expected price volatility related to the inventory levels is a new
channel we identify to show why irreversible investment decisions in a storable commodity market
capture more accurately both price and investment dynamics observed in the data as compared to an
irreversible investment setting without storage capacity.
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| Does a Nexus Exist between Implied Volatility and Storage Regimes in Agricultural Commodities?
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| Alankrita Goswami and Berna Karali |
| Year: 2019 |
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Abstract
Considering that Working curve is a well-established stylized fact and that backwardation exists in the grain markets, we build upon the existing literature to explore the nexus between implied volatility (IV) and storage regimes in substitute agricultural commodity markets. We use a substitute-commodity market-setup of corn and soybean to account for any spillovers across their physical-market fundamentals. The impact of commodity fundamentals (production-related information and storage), macroeconomic indicators and financial market-variables is studied on nearby and deferred implied volatility series; the analysis is carried out both at daily and weekly frequency. In fact, we do find the spillovers across the production-related information disappear in the weekly analysis; thus, suggesting the need to account for early-impact of such information on a daily-basis for modeling the uncertainty levels. The distinct reaction of implied volatility of different maturity periods (i.e., nearby and deferred) to the commodity-fundamentals highlights that not only the two IV series behave differently during episodes of contango and backwardation, but also that they behave differently from each other during the two storage-scenarios. Therefore, our study makes crucial additions to the existing works and emphasizes the need to acknowledge the differing behavior of the nearby and far-out IV levels during episodes of contango and backwardation in the grain markets.
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| The Impact of Brazil on Global Grain Dynamics:
A Study on Cross-market Volatility Spillovers
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| Felipe Grimaldi Avileis and Mindy Mallory |
| Year: 2019 |
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Abstract
We will investigate the evolution of the relationship between Brazilian and Global grain
markets. Through a three step approach, we will test the series for cointegration, proceed with
the adequate modeling (VAR or VECM) and use the residuals of these models to estimate a
BEKK GARCH and relative volatility spillovers across two time periods, before and after Brazil
started double-cropping. Our results indicate no significant cointegration between corn and
soybeans markets before Brazil started double-cropping and significant cointegration after, for
both markets. Volatility spillovers dynamics also changes, from no spillovers to spillovers from
and to Brazil on corn, and from the US spilling over Brazil to Brazil spilling over to the US on
soybeans. Our results are important because they show that the importance of Brazil to global
grain price formation is substantial and risk managers must be aware of it in order to perform
well.
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| Driving Black Sea Grain Prices:Evidence on CBoT Futures and Exchange Rates
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| Maximilian Heigermoser, Linde Gotz, and Tinoush Jamali Jaghdani |
| Year: 2019 |
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Abstract
Over the last two decades, the Black Sea region developed to be a key global exporting region for corn and wheat. However, many market participants grapple with insufficient knowledge of factors that drive Black Sea spot prices, while effective futures markets that could facilitate price discovery and risk management are still missing. In our study, we identify market-specific drivers of volatility of Ukrainian corn and Russian wheat prices. We use daily Black Sea spot price indices for both grains to estimate non-parametric realized volatility measures. These are regressed on several potential drivers, namely, respective futures prices, exchange rates, oil prices and freight rates that serve as a proxy for demand shifts. Estimation results suggest that Ukrainian corn price volatility is well explained by futures price movements and demand shifts, while Russian wheat markets are rather isolated from futures price movements and mostly depend on own lagged volatility and exchange rate movements. Additionally, we find asymmetric responses to price movements at the CBoT: both Black Sea markets react significantly stronger to price increases at the CBoT than to price decreases.
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| Sources of Errors In USDA’s Net Cash Income Forecasts
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| Olga Isengildina Massa, Berna Karali, Todd Kuethe and Ani Katchova |
| Year: 2019 |
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Abstract
This study evaluates the accuracy of net cash income (NCI) forecasts and its components in order to track down the main sources of errors in NCI forecasts over 1986-2017. Specifically, we examine the bias as well as the correlation between errors in net cash income forecasts and in forecasts of its components for each forecasting horizon. Our findings suggest that long term NCI forests underestimate the official estimate. Crop receipts forecasts appear to be the main source of this bias as underestimation in crop receipts persists throughout the forecasting cycle. The main contributors to NCI forecast errors are errors in expenses and in crop and livestock receipts. Errors for all components except farm related income tend to decline over the forecasting cycle. There is not much evidence of forecast errors becoming larger or smaller over time. These findings identify potential areas for improvement in USDA's NCI forecasts.
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| Wheat Futures Trading Volume Forecasting and
the Value of Extended Trading Hours
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| Joseph P. Janzen and Nicolas Legrand |
| Year: 2019 |
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Abstract
Electronic trading in modern commodity markets has extended trading hours, lowered barriers to
listing new contracts, broadened participation internationally, and encouraged entry of new trader
types, particularly algorithmic traders whose order execution is automated. This paper seeks to
understand how these forces have shaped the amount and timing of trading activity using the world's
multiple wheat futures markets as a laboratory. To do so, we extend existing volume forecasting
models found in the literature on volume weighted average price (VWAP) order execution (e.g.
Bialkowski, et al 2008 and Humphery-Jenner 2011) with applications beyond trading algorithm
design. We consider a setting with multiple trading venues for related commodities, specifically the
front-month Chicago Mercantile Exchange Soft Red Wheat and Paris Euronext Milling Wheat futures
contracts. We compare a series of nested forecasting models to infer whether past trading history,
intraday volume dynamics, cross market trading activity, and other information are useful predictors
of trading activity. We assess the value of extended trading hours and the existence of alternative
trading venues by testing whether trading volume is more predictable at particular times throughout
the trading day.
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| The Change in Price Elasticities in the U.S. Beef Cattle Industry and the Impact of Futures Prices in Estimating the Price Elasticities
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| Sei Jeong |
| Year: 2019 |
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Abstract
This paper estimates price elasticities in the U.S. beef cattle industry by using the data for the time period during December 1999 and June 2018. In addition to the adaptive model which was used by many previous studies this study also uses rational expectations model by using futures prices to consider the life cycle of growing cattle. The results show that fed cattle supply is more affected by consumption good criteria rather than capital goods criteria in the short term. The long run own price elasticity for fed cattle supply has increased a lot compared to the estimates from previous studies. It implies that producers' low budget situation caused by several droughts has had a considerable impact on the cattle industry. The results for the feeder cattle demand are consistent with profit maximization behavior of the producers.
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| Fundamentals and Grain Futures Markets
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| Berna Karali, Olga Isengildina-Massa,
and Scott H. Irwin |
| Year: 2019 |
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Abstract
A long-standing puzzle in commodity markets is the low explanatory power of supply and demand
fundamentals for explaining the variability of prices in these markets. We apply an instrumental
variables correction for measurement errors to investigate how noise in the surprise component
of USDA Crop Production reports affects estimated price responses in corn, soybeans, and wheat
futures markets from 1970 to 2016. Our findings demonstrate that after correcting for
measurement error in market surprises, the explanatory power of fundamentals increases about
three-fold and often exceeds 70%. This is compelling evidence that fundamentals are the main
driver of price movements in grain futures markets.
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| An Update and Re-Estimation of the ERS Livestock Baseline Model
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| William E. Maples, B. Wade Brorsen, William F. Hahn, Matthew MacLachlan, and Lekhnath Chalise |
| Year: 2019 |
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Abstract
This report describes a re-estimation of the Economic Research Service (ERS) domestic livestock baseline model. The model consists of production, demand, and price transmission sections for the beef, pork, broiler, and turkey sectors. The updated model largely maintains the overall structure of the current model and mainly focuses on re-estimating the equations with current data. However, major changes were made to the consumer demand part of the baseline model. The current model uses an "inverse demand" model. Inverse demand models take the quantities available to consumers and then calculate the prices that will make consumers buy those quantities. The version presented in this paper uses a quantity dependent system. The equations calculate how much beef, pork, chicken, and turkey consumers will want to buy given the prices of the four meats and consumer income. This updated livestock baseline model could aid the ERS in making ten-year projections for the United States livestock sector.
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| Can Satellite Data Forecast Valuable Information
from USDA Reports ? Evidences on Corn Yield
Estimates
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| Pierrick Piette |
| Year: 2019 |
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Abstract
On the one hand, recent advances in satellite imagery and remote sensing allow one to
easily follow in near-real time the crop conditions all around the world. On the other
hand, it has been shown that governmental agricultural reports contain useful news for
the commodities market, whose participants react to this valuable information. In this
paper, we investigate wether one can forecast some of the newsworthy information
contained in the USDA reports through satellite data. We focus on the corn futures
market over the period 2000-2016. We rst check the well-documented presence of
market reactions to the release of the monthly WASDE reports through statistical tests.
Then we investigate the informational value of early yield estimates published in these
governmental reports. Finally, we propose an econometric model based on MODIS
NDVI time series to forecast this valuable information. Results show that market
rationally reacts to the NASS early yield forecasts. Moreover, the modeled NDVI-based
information is signi cantly correlated with the market reactions. To conclude, we
propose some ways of improvement to be considered for a practical implementation.
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Click here for a copy of the paper in Adobe's PDF format.
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| Evaluation of Ambiguity in Commodity Futures Markets: Analysis of Corn and Coffee Futures Prices
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| Waldemar Souza, Rafael Palazzi, Carlos Heitor Campani, and Martin Bohl |
| Year: 2019 |
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Abstract
Ambiguity, defined as the uncertainty in probability distribution of asset prices resulting from misinterpretation of lack of information, is a current feature of financial assets. There are few empirical studies of ambiguity in financial and commodity futures markets. We define an ambiguity measure of corn and coffee futures daily prices, using the VAR framework to evaluate the autoregressive and cross-impact of the ambiguity and log-returns. Results show that the ambiguity in corn futures prices illustrates a higher impact compared with coffee futures prices, with a possible explanation being the action of non-commercial traders. The knowledge of the ambiguity measure in commodity futures markets can be applied to enhance production, storage, trading and hedging decisions.
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| Cost and Cooperation: The Effects of Section 199 on the Basis Offered by Grain Marketing Cooperatives
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| Andrew Swanson, Anton Bekkerman, and Mykel Taylor |
| Year: 2019 |
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Abstract
The 2004 American Jobs Creation Act created Section 199, a tax provision for producers of domestic goods. During the ensuing decade, Section 199 became especially important for agricultural cooperatives, partly because of a series of favorable Internal Revenue Service private letter rulings for marketing cooperatives. We analyze the impacts of Section 199 on agricultural markets by assessing differential effects on the pricing behavior of grain marketing cooperatives and non-cooperatives in Nebraska and Kansas through using a difference-in-difference empirical strategy and winter wheat basis data. The results indicate that the series of IRS letter rulings in 2008 widened the basis differential between cooperative and non-cooperative firms by almost 5 cents per bushel on average. Furthermore, these market distorting effects are greater for elevator locations that do not have a competing location within 10 miles of their location. While the benefits of Section 199 have been widely touted by cooperative lobbying groups, the results of this paper show the importance of also considering the costs of policy interventions directed at specific agricultural firm types.
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| Intraday Trading Invariance in the Grain Futures
Markets
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| Zhiguang (Gerald) Wang |
| Year: 2019 |
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Abstract
We test the microstructure invariance proposed by Kyle and Obizhaeva (2016) in the
grain markets. Using the CME's intraday best-bid-o er data from 2008 to 2015, we
nd support for both trade size invariance and trading cost invariance at 1-minute,
5-minute, and 10-minute, although not in its original form. After rescaling the trading
activity by spread cost per Benzaquen et al (2016), we nd strong evidence for both
hypotheses of invariance. The ndings help understand the trading dynamics of grain
commodities from both trading and regulatory perspectives. Speci cally, we can derive
the number of trades, trading cost, and illiquidity measure based on observable metrics,
such as price, volume and historical volatility. These imputed measures can be further
used to identify the systematic risks resulting from speculative transactions.
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