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The ethanol mandate and crude oil and biofuel agricultural commodity price dynamics

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  • Serletis, Apostolos
  • Xu, Libo

Abstract

We investigate mean and volatility spillovers between the crude oil market and the main biofuel feedstock markets (corn, soybean, and sugar). In doing so, we estimate a four-variable vector error correction (VEC)–GARCH–in–Mean model with a BEKK representation for the variance equation, and also examine the possible effects of the ethanol mandate by including a dummy variable in both the conditional mean and variance equations. We find that the oil market and the biofuel feedstock markets are tightly interconnected and that the ethanol mandate has strengthened their linkages in terms of volatility spillovers.

Suggested Citation

  • Serletis, Apostolos & Xu, Libo, 2019. "The ethanol mandate and crude oil and biofuel agricultural commodity price dynamics," Journal of Commodity Markets, Elsevier, vol. 15(C), pages 1-1.
    • Handle: RePEc:eee:jocoma:v:15:y:2019:i:c:3
    DOI: 10.1016/j.jcomm.2018.07.001
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    Cited by:

    1. Zhuo Chen & Bo Yan & Hanwen Kang, 2022. "Dynamic correlation between crude oil and agricultural futures markets," Review of Development Economics, Wiley Blackwell, vol. 26(3), pages 1798-1849, August.
    2. Cheng, Natalie Fang Ling & Hasanov, Akram Shavkatovich & Poon, Wai Ching & Bouri, Elie, 2023. "The US-China trade war and the volatility linkages between energy and agricultural commodities," Energy Economics, Elsevier, vol. 120(C).
    3. Declerck, Francis & Hikouatcha, Prince & Tchoffo, Guillaume & Tédongap, Roméo, 2023. "Biofuel policies and their ripple effects: An analysis of vegetable oil price dynamics and global consumer responses," Energy Economics, Elsevier, vol. 128(C).
    4. Roberto Louis Forestal & Shih-Ming Pi, 2021. "Using Artificial Neural networks and Optimal Scaling Model to Forecast Agriculture Commodity Price: An Ecological-economic Approach," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 11(3), pages 1-3.
    5. Jin Zhang & Zhenqing Lin & Jinkai Li, 2024. "Analyzing the risk spillovers of international crude oil on China's corn and biofuel ethanol markets: A transition toward green economy and environmental sustainability," Energy & Environment, , vol. 35(3), pages 1216-1234, May.
    6. Vellachami, Sanggetha & Hasanov, Akram Shavkatovich & Brooks, Robert, 2023. "Risk transmission from the energy markets to the carbon market: Evidence from the recursive window approach," International Review of Financial Analysis, Elsevier, vol. 89(C).
    7. Eissa, Mohamad Abdelaziz & Al Refai, Hisham, 2019. "Modelling the symmetric and asymmetric relationships between oil prices and those of corn, barley, and rapeseed oil," Resources Policy, Elsevier, vol. 64(C).
    8. Derick Quintino & Cristiane Ogino & Inzamam Ul Haq & Paulo Ferreira & Márcia Oliveira, 2023. "An Analysis of Dynamic Correlations among Oil, Natural Gas and Ethanol Markets: New Evidence from the Pre- and Post-COVID-19 Crisis," Energies, MDPI, vol. 16(5), pages 1-14, February.
    9. Zhuo Chen & Bo Yan & Hanwen Kang, 2023. "Price bubbles of agricultural commodities: evidence from China’s futures market," Empirical Economics, Springer, vol. 64(1), pages 195-222, January.
    10. Zhao, Lu-Tao & Liu, Hai-Yi & Chen, Xue-Hui, 2024. "How does carbon market interact with energy and sectoral stocks? Evidence from risk spillover and wavelet coherence," Journal of Commodity Markets, Elsevier, vol. 33(C).
    11. Wei, Yanfeng & Qiu, Feng & An, Henry & Zhang, Xindon & Li, Changhong & Guo, Xiaoying, 2024. "Exogenous oil supply shocks and global agricultural commodity prices: The role of biofuels," International Review of Economics & Finance, Elsevier, vol. 92(C), pages 394-414.
    12. Davison, Matt & Merener, Nicolas, 2023. "Equilibrium and real options in the ethanol industry: Modeling and empirical evidence," Journal of Commodity Markets, Elsevier, vol. 31(C).

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    More about this item

    Keywords

    VEC–GARCH–In–Mean; BEKK model; Mean and volatility spillovers; Structural breaks;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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