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US biofuel market persistence and mean reversion properties

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  • Martin-Valmayor, Miguel A.
  • Gil-Alana, Luis A.
  • Martín, Asís Pardo

Abstract

This paper investigates the market persistence and mean reversion properties for corn, bioethanol and gasoline prices in the US biofuel industry, evaluating long memory effects with fractional integration techniques from January 1982 to May 2022 with USDA data. Empirical results show evidence of no mean reversion properties for the prices in the three series though some support of it is found when the differences of bioethanol and gasoline are taken with respect to corn. Thus, external shocks in the original series are expected to remain persistent and would require additional policy measures to recover the original trend. Furthermore, the impact of Covid on the time series has been analyzed by comparing the scenarios pre and post pandemic, finding evidence of no major changes in the integration orders in all the series under analysis.

Suggested Citation

  • Martin-Valmayor, Miguel A. & Gil-Alana, Luis A. & Martín, Asís Pardo, 2023. "US biofuel market persistence and mean reversion properties," Economic Analysis and Policy, Elsevier, vol. 78(C), pages 648-660.
    • Handle: RePEc:eee:ecanpo:v:78:y:2023:i:c:p:648-660
    DOI: 10.1016/j.eap.2023.04.008
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    References listed on IDEAS

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    1. Sowell, Fallaw, 1992. "Maximum likelihood estimation of stationary univariate fractionally integrated time series models," Journal of Econometrics, Elsevier, vol. 53(1-3), pages 165-188.
    2. Du, Xiaodong & Carriquiry, Miguel A., 2013. "Flex-fuel vehicle adoption and dynamics of ethanol prices: lessons from Brazil," Energy Policy, Elsevier, vol. 59(C), pages 507-512.
    3. Saghaian, Sayed & Nemati, Mehdi & Walters, Cory & Chen, Bo, 2018. "Asymmetric Price Volatility Transmission between U.S. Biofuel, Corn, and Oil Markets," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 43(1), January.
    4. OlaOluwa S. Yaya & Ahamuefula E. Ogbonna & Fumitaka Furuoka & Luis A. Gil‐Alana, 2021. "A New Unit Root Test for Unemployment Hysteresis Based on the Autoregressive Neural Network," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(4), pages 960-981, August.
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    6. Xiaoli Liao Etienne & Andrés Trujillo-Barrera & Seth Wiggins, 2016. "Price and volatility transmissions between natural gas, fertilizer, and corn markets," Agricultural Finance Review, Emerald Group Publishing Limited, vol. 76(1), pages 151-171, May.
    7. Morris, Daniel H. & Parrott, Scott D. & Mehlhorn, Joey & Tewari, Rachna & Pruitt, Ross, 2017. "The Price Transmission Relationship Between Ethanol, Wholesale Gasoline, And Blended Retail Gasoline," International Journal of Food and Agricultural Economics (IJFAEC), Alanya Alaaddin Keykubat University, Department of Economics and Finance, vol. 5(2), April.
    8. Debdatta Pal & Subrata K. Mitra, 2017. "Diesel and soybean price relationship in the USA: evidence from a quantile autoregressive distributed lag model," Empirical Economics, Springer, vol. 52(4), pages 1609-1626, June.
    9. Cuestas Juan Carlos & Gil-Alana Luis Alberiko, 2016. "Testing for long memory in the presence of non-linear deterministic trends with Chebyshev polynomials," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(1), pages 57-74, February.
    10. Naveen Musunuru, 2016. "Examining Volatility Persistence and News Asymmetry in Soybeans Futures Returns," Atlantic Economic Journal, Springer;International Atlantic Economic Society, vol. 44(4), pages 487-500, December.
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    More about this item

    Keywords

    Bioethanol; Corn; Fractional integration; Long memory; Time series analysis;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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