A Graphical and Beta Analysis of the Effect of Increased Ethanol Production on the Volatility of Corn Prices

The rapid upward shift in ethanol demand has raised concerns about ethanol’s impact on the price level and volatility of agricultural commodities. The popular press attributes much of this volatility in commodity prices to a price bubble in ethanol fuel and recent deflation. Market economics predicts not only a softening of demand to high commodity prices but also a positive supply response. This volatility in ethanol and commodity prices are investigated using cointegration, vector error corrections (VECM), and multivariate generalized autoregressive conditional heteroskedascity (MGARCH) models. In terms of derived demand theory, results support ethanol and oil demands as derived demands from vehicle-fuel production. Gasoline prices directly influence the prices of ethanol and oil. However, of greater significance for the fuel versus food security issue, results support the effect of agricultural commodity prices as market signals which restore commodity markets to their equilibriums after a demand or supply event (shock). Such shocks may in the short-run increase agricultural commodity prices, but decentralized freely operating markets will mitigate the persistence of these shocks. Results indicate in recent years there are no long-run relations among fuel (ethanol, oil and gasoline) prices and agricultural commodity (corn and soybean) prices.

This study determines the impact of U.S. government policies on U.S. ethanol market and its consequences for the U.S. corn, sugar, and HFCS markets. Using corn as the primary input in ethanol and HFCS production in the United States, along with the substitutability between sugar and HFCS, has linked the U.S. ethanol market to the U.S. HFCS, sugar, and corn markets. To address the problem, two sets of data, quarterly and annual data, were collected and a simultaneous econometric model was constructed. Estimated results show that the “2007 Energy Independence and Security Act” will increase the domestic corn price and ethanol and HFCS production costs. Increases in HFCS production costs decrease the comparative advantage of HFCS over sugar and will encourage HFCS users to replace HFCS with sugar. HFCS will lose its comparative advantage over domestic raw sugar after 2009. Without government policies that mandate consumption levels for ethanol, depending on gasoline and corn prices, maximum corn-based ethanol production would be between 1.5 and 19.6 billion gallons per year in year 2015. In the case of having “mandatory ethanol consumption,” there will be a minimum quantity of ethanol consumption and production, equal to 15 billion gallons per year in 2015. Depending on the relative levels of corn and gasoline prices, annual corn-based ethanol production will be between 15 and 19.6 billion gallons in 2015. With regards to the profitability of sugar-based ethanol production, the U.S. sugar support program plays a critical role. Using raw sugar, at world sugar price levels, for producing ethanol, sugar can compete with corn when corn prices reach $5.49 per bushel, when the ethanol production level approaches 9.3 billion gallons annually. With the sugar support program in force, raw and refined sugar cannot compete with corn in the near future. Removal of the sugar import quota decreases sugar production and price while sugar imports and consumption increase. This allows sugar to be considered as a viable feedstock for the production of ethanol. Using sugar for ethanol production reduces the amount of corn needed for ethanol production, suppresses the corn price, and stabilizes the corn market.

Food price volatility has re-emerged as an important topic of political discussion since the food price crisis of 2007/08. Not only the observation of increasing price levels but also their apparent increased volatility on key markets (most notably grains) has triggered many studies both at the conceptual and the empirical level. Since people suffer from high and unstable prices, especially in least developed countries, this development has been widely recognized as a global problem, and a major impediment to combating hunger and malnutrition. This thesis aims to contribute to the debate on how best to cope with agricultural commodity price volatility. To gain a comprehensive overview of agricultural price volatility, its causes, and potential ways to help affected market participants in a meaningful way, this thesis focuses on three major aspects that built the three main chapters of this cumulative dissertation: Chapter 2 aims to robustly answer the question of how volatility has developed since the food price crisis of 2007/2008. General differences in volatility level, volatility of volatility, and volatility persistence for a set of realized, GARCH model-based and implied volatilities are noted for three agricultural commodities – wheat, corn, and soybeans. Moreover, common statements regarding the increase of volatility since the food price crisis of 2007/2008 and further relevant issues such as changes in volatility persistence and quantification of the increase are analyzed in terms of a robust conclusion. Chapter 3 identifies drivers of volatility for several oilseed and vegetable oil markets. The chapter provides an investigation of the joint effects of fundamental volatility drivers and spillover effects between related markets. Chapter 4 introduces a set of related risk measures to characterize the detailed structure of volatility in agricultural commodity markets. These measures allow for a decomposition of overall price moves into “large" changes with potentially severe economic consequences and “normal" changes. Forward-looking estimators of the risk measures that extract market expectations about future commodity price moves from current option prices are derived. An empirical study on major grain markets demonstrates the forecasting power of the implied estimators. Overall, this thesis demonstrates that managing risk and mitigating the impacts of excessive price volatility can only be successful if one is aware of which commodity markets are affected, which specific kind of price risk one faces, and consequently which group of market participants needs protection, and if this risk is recognized early enough to undertake helpful measures.

Using the competitive storage model to estimate the impact of ethanol and fueling investment on corn prices

By diverting agricultural land away from food, feed, and livestock production, increased production of biofuel feedstock crops tend to drive up prices for agricultural commodities. But by how much? This question has been heavily debated in recent years, following the food price crisis of 2007–2008. A systematic review of 121 studies that quantifies the impact of biofuel demand on agricultural commodity markets reveals that there is still considerable uncertainty around the exact magnitude of the price response. Increased demand for corn ethanol in the United States—the focus of the majority of studies—is estimated to have accounted for 14–43% of the rise in US corn prices in the period 2000–2008. The divergence in results between studies is mainly due to different assumptions regarding demand and supply elasticities for agricultural commodities, and there is very limited empirical evidence that can help reduce the uncertainty around the value of these parameters, especially outside the United States. Few studies analyze the impact of biofuel demand beyond current or near‐future levels and it is argued that estimated price effects can neither be extrapolated to large‐scale biofuel demand shocks, nor are most models able to capture accurately the impacts of such shocks due to weaknesses in how land markets and land transformation process are modeled. To better gauge current and future impacts of biofuel demand on agricultural commodity markets, we need better data on supply and demand responses, both in the short and long run, as well as improved modeling of land competition and land‐use change. WIREs Energy Environ 2015, 4:410–428. doi: 10.1002/wene.155This article is categorized under: Bioenergy > Economics and Policy Energy and Development > Economics and Policy

U.S. ethanol expansion objectives are to improve both energy security and the environmental. However, this expansion has raised issues concerning its detrimental impacts on the price volatility of developing countries’ agricultural commodities. These concerns are addressed by empirically investigating the relations among U.S. ethanol and corn markets with developing countries’ corn prices. Results indicate that U.S. ethanol demand impacts on developing countries’ corn prices vary by country. Further, results reveal that the transmission effects of U.S. ethanol shocks are systematically stronger for countries with higher food import dependency and U.S. food aid.

Early forecasting of corn yield and price variations using satellite vegetation products

Purpose- The purpose of this study is to examine the long and short-term relationship between Bitcoin and altcoins selected based on their market capitalization through an empirical analysis. For this purpose, the daily data of Bitcoin and nine altcoins consisting of Ether, Ripple, Tether, Litecoin, Monero, Stellar, Dash, Nem, Dogecoin for the period 07/08/2015-08/01/2020 were used. Methodology- The long-run relationship between Bitcoin and altcoins is first analyzed by Vector Autoregression (VAR) analysis. Granger causality test was utilized to determine the short-run causality relationship between the variables. The tests were conducted with the Eviews program. Findings- According to the results of the VAR analysis conducted to investigate the long-run relationship, there is a long-run relationship between Dogecoin, Dash, Litecoin, Nem, Stellar and Ripple and Bitcoin. After determining the long-run relationship between the variables, the relationships between the variables were analyzed with the help of impulse response functions. Impulse response function shows the effect of a one-unit shock to one variable on the other variable. Accordingly, when the results of impulse response functions are analyzed; it is seen that a one-unit random shock in Bitcoin has a negative effect on Ripple, Nem, Litecoin, Dash, Litecoin, Dogecoin in the first two periods, the effect decreases in the second period, and this effect disappears in the third period. A random shock to Bitcoin causes a positive effect on Stellar that lasts for two periods. This positive effect ends in the third period. After analyzing the relationship between Bitcoin and altcoins with impulse response functions, the source of the changes in the variance of the variables is analyzed through variance decomposition. According to the variance decomposition results, the effect of Bitcoin on Dogecoin is 25% in the first period and 22% in the other periods. The variance decomposition of Dash shows that approximately 18% of the change in standard deviation was caused by Bitcoin in the first period and this percentage increased to 25.5% in the following periods. Litecoin's variance decomposition results show that 33% of the change in standard deviation from the first period to the last period was caused by Bitcoin. It is observed that approximately 8% of the change in Nem's standard deviation in the first period was caused by Bitcoin, while this rate increased to 21.5% in the last period. From the first period to the last period, 13.5% of the change in Stellar's standard deviation was caused by Bitcoin. When the variance decomposition of Ripple is analyzed, it is observed that 10% of the difference in the standard deviation is due to Bitcoin. This situation continued similarly from the first period to the last period. Following the VAR analysis, Granger causality test was conducted to explain the short-term relationship between the variables. According to the test results, there is a bidirectional Granger causality between Bitcoin and all altcoins. Accordingly, when Bitcoin is taken as the dependent variable, it is the Granger cause of Ether, Ripple, Tether, Litecoin, Monero, Stellar, Dash, Nem, Dogecoin. When the Granger causality relationship between altcoins is analyzed, a causality relationship was observed from Tether to Stellar, while no causality was found from Stellar to Tether. Similarly, while Granger causality is observed from Tether to Ripple, there is no causality from Ripple to Tether. The variance decomposition of Stellar and Ripple shows that Tether does not contribute to the change in standard deviation. The variance decomposition test supports the Granger test results. All altcoin variables except these are Granger causes of each other. Conclusion- At the end of the study, according to the results of the VAR analysis to determine the long-run relationship, there is a long-run relationship between Dogecoin, Dash, Litecoin, Nem, Stellar and Ripple and Bitcoin. There is no long-run relationship between Tether, Monero, Ether and Bitcoin. According to the Granger causality analysis test results conducted to observe the short-term relationship, there is a bidirectional Granger causality between Bitcoin and all altcoins. Accordingly, when Bitcoin is taken as the dependent variable, it is the Granger cause of Ether, Ripple, Tether, Litecoin, Monero, Stellar, Dash, Nem, Dogecoin. As a result, it is observed that Bitcoin has a short-term relationship with all 9 altcoins subject to the study, and a long-term relationship with Dogecoin, Dash, Litecoin, Nem, Stellar and Ripple. These results show that the price movements in Bitcoin have an impact on altcoins. Keywords: Bitcoin, altcoin, cryptocurrency, causality analysis, VAR analysis. JEL Codes: G17, G10, C58

Corn is an essential grain in the Mexican culinary, cultural and social heritage. However, the volatility in corn prices brings uncertainty to agricultural farmers and has caused an increase in imports of the grain from other countries. The purpose of this study is to use time-series models regularly applied in finance to an agricultural commodity and forecast corn prices in Mexico. The study employs autoregressive integrated moving average (ARIMA) models to forecast prices in 2024 and 2025 using data on average rural prices of grain corn from the period 1980 to 2023 The results contribute to a theoretical discussion on employing statistical tools to reduce market uncertainty on agricultural commodities and provide empirical practical results on corn prices for decision making. The results are innovative in using the ARIMA statistical tool to analyze a specific commodity (corn) in a specific market (Mexico). The conclusions of the study suggest an upward trend in corn prices for 2024 and 2025, however, price stagnation and uncertainty is observed. Although government policies have introduced price guarantees for corn in Mexico, they only cover less than 3% of total production. Future studies should analyze price divergence by regions or states in Mexico.

Volatility and seasonality of feed grain prices and cattle performance contribute to variability of steer feeding cost-of-gain. This study was conducted to determine the relative impacts of feed grain prices and cattle performance on cattle feeding cost-of-gain. Closeout data on 7292 pens of steers placed on feed from January 1980 through May 1991 in two western Kansas feedyards were investigated. Three different placement weight categories were studied on a pen basis. Regression analysis was used to determine the relative impact of corn (Zea mays L.) price, feed conversion, and average daily gain on cost-of-gain variability. Coefficients of separate determination were calculated to allocate the total explained cost-of-gain variability attributable to each of the three components [...]

The rise of biofuels as a mainstream fuel source coupled with large price fluctuations in both crude oil and agricultural commodity markets in recent years has led to several investigations into the effect crude oil prices exert over agricultural commodity prices. This study analyses crude oil, corn, soybean and sugar prices from 1980 to 2012 and applies time series econometric methods to determine the equilibrium relationship and causality among the commodity prices. The study then examines the adjustment dynamics of the prices after an exogenous shock to oil prices. The results show that oil only plays a small role in the long-run equilibrium relationship among the grain commodities; however, there is strong evidence that the oil price shocks affect agricultural commodity prices in the short to medium term. An exogenous shock to oil prices is found to affect sugar and corn prices, the two largest inputs into bio-ethanol globally.

The objective of the paper is to present the price interdependencies between agricultural commodity products from Ukraine (both export and non-export oriented) and other commodities whose prices are shaped on world markets, with a special focus on the role of their volatility. The research demonstrates a tight connection between the global prices of crude oil and prices of Ukrainian corn and wheat. Additionally, the volatility of world prices of agricultural commodities influenced the Ukrainian national market and had significant impact on domestic price declines. At the same time, the mechanisms for pricing non-export related agricultural commodities are formed mostly under the influence of factors from the domestic market. It is argued that a low interdependency between non-export oriented agricultural commodities and world markets stipulates the social stability of Ukraine's population. (original abstract)

The outlook for U.S. corn markets is inextricably linked to what happens to the U.S. ethanol industry, which depends, in turn, on the level of government subsidies and mandates. We develop a stochastic partial equilibrium model to simulate outcomes for the corn market for the 2008/09 marketing year to gain insight into these linkages. The model includes five stochastic variables that are major contributors to corn price volatility: planted acreage, corn yield, export demand, gasoline prices, and capacity of the ethanol industry. Our results indicate that integration of gasoline and corn markets has increased corn price volatility and that the passage of the expanded ethanol mandates in the Energy Independence and Security Act (EISA) has had modest effects on corn prices. Model results indicate an expected average marketing year price of $4.97 per bushel and a price volatility of 17.5% without the 10 billion gallon EISA mandate but with maintenance of the $0.51-per-gallon tax credit. Imposition of the mandate increases the expected price by 7.1% and price volatility by 12.1%. The effects of the mandate are modest, as ethanol production would average 9.5 billion gallons without the mandate because of high gasoline prices. The mandate is binding with a probability of 37.8%, which indicates that an additional tax or subsidy will be needed to ensure that the mandate is met. High corn prices caused by drought can cause the mandate to bind. Fixing 2008 corn yields at extreme drought levels increases expected corn prices to $6.59 per bushel without a mandate and to $7.99 per bushel with the EISA mandate. An average additional subsidy of $0.73 per gallon of ethanol would be needed to ensure that the mandate is met in this drought scenario. Elimination of the current blenders tax credit would result in the mandate not being met in all cases. On average, a subsidy of $0.41 per gallon would ensure that ethanol production is at least 10 billion gallons in the 2008/09 marketing year.

The correlation between Chinese and international commodity prices may be nonlinear because of China’s minimum agricultural commodity purchase price policy and temporary storage policy. In order to research this nonlinear dynamic correlation mechanism, we construct a nonlinear Granger causality test model and a nonlinear autoregressive distribution lag model including Chinese and international agricultural commodity (soybean, corn, rice, and wheat) price variables. Our empirical results reveal that a unidirectional causal relation exists between international and Chinese prices for soybeans and corn; specifically, international prices of soybeans and corn Granger-cause Chinese prices of soybeans and corn. Moreover, the pass-through effects between Chinese and international commodity prices are asymmetric; Chinese agricultural commodity prices respond more strongly to positive shocks than negative shocks of international agricultural commodity prices.

Volatility of international commodity prices in times of COVID-19: Effects of oil supply and global demand shocks