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Crude Oil and Biofuel Agricultural Commodity Prices

In: Uncertainty, Expectations and Asset Price Dynamics

Author

Listed:
  • Semei Coronado

    (Universidad de Guadalajara)

  • Omar Rojas

    (Universidad Panamericana)

  • Rafael Romero-Meza

    (Universidad Alberto Hurtado)

  • Apostolos Serletis

    (University of Calgary)

  • Leslie Verteramo Chiu

    (Cornell University)

Abstract

Crop prices in the United States (USA), and especially corn prices, have been displaying important changes in the last 10 years, after the ethanol mandate in 2005. Motivated by these significant price changes, there has been a growing interest in the study of price transmission from oil prices to agricultural commodity prices. In this contribution, we concentrate on the relationship between the price of oil and the prices of three agricultural commodities that are used for biofuels production: corn, soybeans, and sugar. In doing so, we apply linear Granger causality tests, the nonlinear causality test of Diks and Panchenko (J Econ Dyn Control 30:1647–1669, 2006), and the Brooks and Hinich (J Empir Financ 6:385–404) cross-bicorrelation test to daily data over the period from 1990 to 2016. Coherent with the previous studies, we find weak linear Granger causality, but strong bidirectional nonlinear causality, especially for the period from 2006 to 2016. Using the Brooks and Hinich test, we also identify the number of epochs (nonoverlapped windows) where there is nonlinear dependence between each pair of series. We find that most cross-bicorrelation windows coincide from 2006 to 2016, indicating that the nonlinear dynamics between the series studied have changed in recent years in the aftermath of the ethanol mandate. Our results provide hints in order to improve our understanding of the effects of the implemented policies in the energy sector on agricultural commodities.

Suggested Citation

  • Semei Coronado & Omar Rojas & Rafael Romero-Meza & Apostolos Serletis & Leslie Verteramo Chiu, 2018. "Crude Oil and Biofuel Agricultural Commodity Prices," Dynamic Modeling and Econometrics in Economics and Finance, in: Fredj Jawadi (ed.), Uncertainty, Expectations and Asset Price Dynamics, pages 107-123, Springer.
    • Handle: RePEc:spr:dymchp:978-3-319-98714-9_5
    DOI: 10.1007/978-3-319-98714-9_5
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    Citations

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    Cited by:

    1. Waseem Khan & Vishal Sharma & Saghir Ahmad Ansari, 2022. "Modeling the dynamics of oil and agricultural commodity price nexus in linear and nonlinear frameworks: A case of emerging economy," Review of Development Economics, Wiley Blackwell, vol. 26(3), pages 1733-1784, August.
    2. Mourad Zmami & Ousama Ben-Salha, 2019. "Does Oil Price Drive World Food Prices? Evidence from Linear and Nonlinear ARDL Modeling," Economies, MDPI, vol. 7(1), pages 1-18, February.
    3. Duc Hong Vo & Tan Ngoc Vu & Anh The Vo & Michael McAleer, 2019. "Modeling the Relationship between Crude Oil and Agricultural Commodity Prices," Energies, MDPI, vol. 12(7), pages 1-41, April.
    4. Tan Ngoc Vu & Chi Minh Ho & Thang Cong Nguyen & Duc Hong Vo, 2020. "The Determinants of Risk Transmission between Oil and Agricultural Prices: An IPVAR Approach," Agriculture, MDPI, vol. 10(4), pages 1-14, April.
    5. 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.
    6. Monika Roman & Aleksandra Górecka & Joanna Domagała, 2020. "The Linkages between Crude Oil and Food Prices," Energies, MDPI, vol. 13(24), pages 1-18, December.
    7. Shahzad, Farrukh & Bouri, Elie & Mokni, Khaled & Ajmi, Ahdi Noomen, 2021. "Energy, agriculture, and precious metals: Evidence from time-varying Granger causal relationships for both return and volatility," Resources Policy, Elsevier, vol. 74(C).
    8. Anita Konieczna & Kamil Roman & Monika Roman & Damian Śliwiński & Michał Roman, 2020. "Energy Efficiency of Maize Production Technology: Evidence from Polish Farms," Energies, MDPI, vol. 14(1), pages 1-20, December.
    9. Yuntong Liu & Yu Wei & Yi Liu & Wenjuan Li, 2020. "Forecasting Oil Price by Hierarchical Shrinkage in Dynamic Parameter Models," Discrete Dynamics in Nature and Society, Hindawi, vol. 2020, pages 1-12, December.
    10. Bin Xu & Boqiang Lin, 2021. "Large fluctuations of China's commodity prices: Main sources and heterogeneous effects," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 2074-2089, April.

    More about this item

    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
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products
    • Q13 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Markets and Marketing; Cooperatives; Agribusiness
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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