Relationship among Energy, Bioenergy, and Agricultural Commodity Prices: Re-Considering Structural Changes

Nemati, Mehdi

doi:10.2139/ssrn.2932341

Cited by 8 publications

(7 citation statements)

References 10 publications

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“…Myers et al (2014)studied the comovements between feedstock and energy prices and found no comovements between these prices in the long run. Nemati (2016) investigated the relationships among the prices of gasoline, ethanol, and agricultural products that included soybeans and corn, using monthly data from January 1986 to November 2014. The result indicated that there was a relationship between energy and agricultural product prices and this relationship intensified during the last decade.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

Asymmetric Price Volatility Interaction between U.S. Food and Energy Markets

et al. 2017

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Links between agricultural commodity and energy prices have become more complex with increased ethanol production. The concerns are whether the new corn-ethanol links lead to volatility-spillover effects between food and energy prices and different data-frequencies is the reason for previous inconsistent results. We investigate the asymmetric volatility-spillover effects between U.S. feedstock and biofuel prices, using an asymmetric BEKK-multivariate-GARCH approach, with daily, weekly, and monthly futures-price frequencies. The results show asymmetric volatility-spillover effects between corn and ethanol prices, and the volatility of corn and ethanol returns respond differently to positive and negative shocks, demonstrating asymmetric volatility transmission, depending on different data-frequencies.

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Section: Background and Literature Reviewmentioning

confidence: 99%

Asymmetric Price Volatility Interaction between U.S. Food and Energy Markets

et al. 2017

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“…Adaptation to the demand-driven mechanism caused by speculative price manipulation, including input, is severely affected by the limitation of the level of flexibility often incompatible with the characteristics and potential of agricultural production and the degree of effective absorption in the market. Analysing the relationship between energy prices and agricultural products by applying the self-regressive vector model (VECM), Nemati (2017) clearly demonstrates the existence of a long-term decisive relationship between these price categories, especially an intensification for the period 2007-2014.…”

Section: Introductionmentioning

confidence: 99%

A short descriptive analysis of the European evolutions of input price indices of agricultural products between 2008-2017: Patterns, trends and implications

Vasile¹,

Radulescu²,

Chivu³

et al. 2022

Strategic Management

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Background: The evolutions of the inputs price and investment indices of the products in agriculture are not only determinant elements in understanding the fluctuations of the food price and the market instability, specific to the agricultural sector but also affects the agricultural production and traceability. Analyzing the European evolutions of the inputs price indices of agricultural products offer the possibility to understand the main trends and tendencies in the agricultural system by reviling the main trend tenancies during a nine year period long. Purpose: The main aim of the study is to investigate the evolution of input price indices of agricultural products in order to underline the specific patterns, trends and implications of the agricultural policies. In addition, the research pays a special attention to the investigation of the Romanian agricultural policy evolution on the most relevant time frame of economic conformity with the European agricultural model. The descriptive analysis is based on the specific annual datasets of price indices of the means of inputs in agricultural production, and the index of real prices of goods and services for investments in agriculture during 2008 - 2017, reported to 2010 as the baseline year. Findings/conclusions: The analyses confirms that the agricultural sector evolution has generated significant input and investment prince changes and unprecedented trend evolutions that led to the massive changes on the agricultural pattern. We strongly advocate and recommend for promoting a solid capacity and durable agricultural production systems and policies through sustainable and long term investments in order to avoid disruptive tendencies in the agricultural market system. Limitations/future research: The research explore the evolutions of the inputs price and investments indices of the products in the European agriculture only form the descriptive analysis without covering an extensive framework or considering other additional variables which consist the main limitation of this study. In a future research the authors will address and extend the research framework by inserting additional variables and items and propose a large and integrative model of analysys.

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“…This has profound practical significance and important value for promoting the iterative innovation of the corn industry and the increase of corn production income. At present, after summarizing the problems and lessons in the past, the corn futures market in our country continues to increase in scale and the market operation is becoming more stable [11].…”

Section: Introduction To Corn Futures and Corn Futures Marketmentioning

confidence: 99%

Guarantee Function of Corn Futures to the Development of Corn Planting Industry

2020

AJAS

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With the development of the market economy, the corn planting industry needs to establish a complete futures system to enter the market safely and steadily. At the same time, due to multiple factors, the function of China's corn futures market needs to be improved. Therefore, it is the current requirement to study the guarantee role of corn futures on the development of the corn planting industry. In this paper, quantitative analysis method is used for cointegration test. The corn futures index price is used as an independent variable, and the corn price is used as a dependent variable to construct a regression model. Corresponding to the agricultural futures market price under the risk measurement model, to explore the hedging function and risk avoidance function of the corn futures market. The results of this study show that the J-B test of corn futures yields is meaningful at around 1%, while the kurtosis is significantly greater than 3 at the 1% insignificance level. The correlation coefficients of spot prices are 0.9067, 0.9243 and 0.8769 respectively. Through a comprehensive analysis, it can be concluded that the volatility in the three periods of the Chinese corn futures market is continuous, completely showing the statistical characteristics of the price of the corn futures market. By comparison, under the influence of the financial crisis, the risk avoidance function of corn futures has declined sharply, and external shocks have extended the duration of futures price changes.

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Relationship among Energy, Bioenergy, and Agricultural Commodity Prices: Re-Considering Structural Changes

Cited by 8 publications

References 10 publications

Asymmetric Price Volatility Interaction between U.S. Food and Energy Markets

Asymmetric Price Volatility Interaction between U.S. Food and Energy Markets

A short descriptive analysis of the European evolutions of input price indices of agricultural products between 2008-2017: Patterns, trends and implications

Guarantee Function of Corn Futures to the Development of Corn Planting Industry

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