Ethanol, Corn, and Soybean Price Relations in a Volatile Vehicle-Fuels Market

By Zibin Zhang, L. Lohr, C. Escalante, M. Wetzstein. 2009

181 citations recorded by the source corpus.

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.

Subfields: Economics and Econometrics, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/bb503bbe7ef37487092590df66dc1e80d22d15e4

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Citations in the corpus

Global Oil Prices and Local Food Prices: Evidence from East Africa (2016, unknown): “This is consistent with numerous other recent papers that find no strong causal link between oil prices and maize prices at world markets (Zhang et al. 2007, 2009, 2010, Gilbert 2010, Serra et al. 2011, Enders and Holt 2012, Zilberman et al. 2013).”
Hedging emerging market stock prices with oil, gold, VIX, and bonds: A comparison between DCC, ADCC and GO-GARCH ☆ (2015, unknown): “Hammoudeh and Yuan, 2008; Hammoudeh et al., 2011; Creti et al., 2013; Choi and Hammoudeh, 2010; Zhang et al., 2009; Du et al., 2011; Serra, 2011; Serra et al., 2011; Nazlioglu et al., 2013; Mensi et al., 2013; Mensi et al., 2014; Beckmann and Czudaj, 2014). review of relevant papers that focus…”
Impacts of ethanol policy on corn prices: A review and meta-analysis of recent evidence (2015, methodology): “Crop price changes are likely to have bigger impacts on consumers in developing countries because they typically rely more heavily on raw agricultural products and spend a greater portion of their incomes on staple foods (Roberts and Schlenker 2013; Zhang et al., 2009a; Runge and Senauer, 2007).”
The Impact of Biofuels on Commodity Food Prices: Assessment of Findings (2013, methodology): “The study by Zhang et al. (2010) used monthly price data for corn, rice, soybeans, sugar, and wheat as well as ethanol, gasoline, and oil between 1981 to 2007 to estimate”
Time-Frequency Dynamics of Biofuels-Fuels-Food System (2012, methodology): “In the previous studies, Zhang et al. (2009, 2010) use VECM and mGARCH models to analyze the US ethanol connections with corn, soybeans and gasoline to find no long-range relationships.”
Volatility linkages between energy and agricultural commodity prices (2016, methodology): “Zhang et al. (2009) do not nd signi cant integration between the U.S. agricultural and energy markets in the early 2000s.”
Volatility Spillovers in U.S. Crude Oil, Ethanol, and Corn Futures Markets (2012, methodology): “Consistent with Zhang et al. (2009), they find no evidence of spillover for the earlier portion of their sample (through October 2006).”
Volatility Spillovers between Food and Energy Markets, A Semiparametric Approach (2011, methodology): “Both Serra et al. (2011) and Zhang et al. (2009) articles characterize the volatility of multiple economic time-series using parametric MGARCH models that have two characteristic features.”