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A cointegration analysis of agricultural, energy and bio-fuel spot, and futures prices

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  • David E. Allen
  • Chialin Chang
  • Michael McAleer
  • Abhay K Singh

Abstract

This article features an analysis of the cointegration relationships among agricultural commodity, ethanol and Cushing crude oil spot, and future prices. The use of grains for the creation of bio-fuels has sparked fears that these demands are inflating food prices. We analyse approximately 10 years of daily spot and futures prices for corn, wheat, sugar ethanol, and oil prices from Datastream for the period 19 July 2006 to 2 July 2015. The analysis features Engle-Granger pairwise cointegration and partial cointegration. Pairs of series, that are cointegrated, are analysed using Markov-switching VECM and Impulse Response Analysis, which confirms that these markets have significant linkages that vary according to whether they are in low or high volatility regimes.

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  • David E. Allen & Chialin Chang & Michael McAleer & Abhay K Singh, 2018. "A cointegration analysis of agricultural, energy and bio-fuel spot, and futures prices," Applied Economics, Taylor & Francis Journals, vol. 50(7), pages 804-823, February.
    • Handle: RePEc:taf:applec:v:50:y:2018:i:7:p:804-823
    DOI: 10.1080/00036846.2017.1340581
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    2. Ji, Qiang & Bouri, Elie & Roubaud, David & Shahzad, Syed Jawad Hussain, 2018. "Risk spillover between energy and agricultural commodity markets: A dependence-switching CoVaR-copula model," Energy Economics, Elsevier, vol. 75(C), pages 14-27.
    3. 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).
    4. Palazzi, Rafael Baptista & Meira, Erick & Klotzle, Marcelo Cabus, 2022. "The sugar-ethanol-oil nexus in Brazil: Exploring the pass-through of international commodity prices to national fuel prices," Journal of Commodity Markets, Elsevier, vol. 28(C).
    5. Zhuo Chen & Bo Yan & Hanwen Kang & Liyu Liu, 2023. "Asymmetric price adjustment and price discovery in spot and futures markets of agricultural commodities," Review of Economic Design, Springer;Society for Economic Design, vol. 27(1), pages 139-162, February.
    6. Janda, Karel & Kravec, Peter, 2022. "VECM Modelling of the Price Dynamics for Fuels, Agricultural Commodities and Biofuels," EconStor Preprints 259404, ZBW - Leibniz Information Centre for Economics.
    7. Curtis McKnight & Feng Qiu & Marty Luckert & Grant Hauer, 2021. "Prices for a second‐generation biofuel industry in Canada: Market linkages between Canadian wheat and US energy and agricultural commodities," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 69(3), pages 337-351, September.
    8. József Popp & Judit Oláh & Mária Farkas Fekete & Zoltán Lakner & Domicián Máté, 2018. "The Relationship Between Prices of Various Metals, Oil and Scarcity," Energies, MDPI, vol. 11(9), pages 1-19, September.
    9. Puneet Vatsa, 2022. "Do crop prices share common trends and common cycles?," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 66(2), pages 363-382, April.
    10. Tsuji, Chikashi, 2018. "New DCC analyses of return transmission, volatility spillovers, and optimal hedging among oil futures and oil equities in oil-producing countries," Applied Energy, Elsevier, vol. 229(C), pages 1202-1217.
    11. Donald Lien & Ziling Wang & Xiaojian Yu, 2021. "Optimal quantile hedging under Markov regime switching," Empirical Economics, Springer, vol. 60(5), pages 2177-2201, May.
    12. Tan Ngoc Vu & Duc Hong Vo & Chi Minh Ho & Loan Thi-Hong Van, 2019. "Modeling the Impact of Agricultural Shocks on Oil Price in the US: A New Approach," JRFM, MDPI, vol. 12(3), pages 1-27, September.
    13. Martinho, V.J.P.D., 2020. "Relationships between agricultural energy and farming indicators," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).

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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • Q02 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Commodity Market
    • Q35 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Nonrenewable Resources and Conservation - - - Hydrocarbon Resources
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources

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