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Time-Frequency Dynamics of Biofuels-Fuels-Food System

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Listed:
  • Lukas Vacha
  • Karel Janda
  • Ladislav Kristoufek
  • David Zilberman

Abstract

For the first time, we apply the wavelet coherence methodology on biofuels (ethanol and biodiesel) and a wide range of related commodities (gasoline, diesel, crude oil, corn, wheat, soybeans, sugarcane and rapeseed oil). This way, we are able to investigate dynamics of correlations in time and across scales (frequencies) with a model-free approach. We show that correlations indeed vary in time and across frequencies. We find two highly correlated pairs which are strongly connected at low frequencies - ethanol with corn and biodiesel with German diesel - during almost the whole analyzed period (2003-2011). Structure of correlations remarkably changes during the food crisis - higher frequencies become important for both mentioned pairs. This implies that during stable periods, ethanol is correlated with corn and biodiesel is correlated with German diesel mainly at low frequencies so that they follow a common long-term trend. However, in the crisis periods, ethanol (biodiesel) is lead by corn (German diesel) even at high frequencies (low scales), which implies that the biofuels prices react more rapidly to the changes in their producing factors.

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  • Lukas Vacha & Karel Janda & Ladislav Kristoufek & David Zilberman, 2012. "Time-Frequency Dynamics of Biofuels-Fuels-Food System," Papers 1209.0900, arXiv.org.
  • Handle: RePEc:arx:papers:1209.0900
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    References listed on IDEAS

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    Cited by:

    1. Piroli, Giuseppe & Rajcaniova, Miroslava & Ciaian, Pavel & Kancs, d׳Artis, 2015. "From a rise in B to a fall in C? SVAR analysis of environmental impact of biofuels," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 921-930.
    2. Capitani, Daniel Henrique Dario, 2014. "Biofuels versus food: How much Brazilian ethanol production can affect domestic food prices," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 170267, Agricultural and Applied Economics Association.
    3. Fernandez-Perez, Adrian & Frijns, Bart & Tourani-Rad, Alireza, 2016. "Contemporaneous interactions among fuel, biofuel and agricultural commodities," Energy Economics, Elsevier, vol. 58(C), pages 1-10.
    4. Bastianin, Andrea & Galeotti, Marzio & Manera, Matteo, 2014. "Causality and predictability in distribution: The ethanol–food price relation revisited," Energy Economics, Elsevier, vol. 42(C), pages 152-160.
    5. Reboredo, Juan C. & Rivera-Castro, Miguel A. & Ugolini, Andrea, 2017. "Wavelet-based test of co-movement and causality between oil and renewable energy stock prices," Energy Economics, Elsevier, vol. 61(C), pages 241-252.
    6. Bastianin, Andrea & Galeotti, Marzio & Manera, Matteo, 2016. "Ethanol and field crops: Is there a price connection?," Food Policy, Elsevier, vol. 63(C), pages 53-61.
    7. Deborah Bentivoglio & Adele Finco & Mirian Rumenos Piedade Bacchi, 2016. "Interdependencies between Biofuel, Fuel and Food Prices: The Case of the Brazilian Ethanol Market," Energies, MDPI, Open Access Journal, vol. 9(6), pages 1-16, June.
    8. Bentivoglio, Deborah & Finco, Adele & Bacchi, Mirian, 2015. "Examining Price Transmission between Fuels and Food Prices: the Brazilian Sugar-Ethanol Market," 2015 Conference, August 9-14, 2015, Milan, Italy 211327, International Association of Agricultural Economists.
    9. Ladislav Kristoufek & Karel Janda & David Zilberman, 2015. "Co-movements of Ethanol Related Prices: Evidence from Brazil and the USA," CAMA Working Papers 2015-11, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    10. Chrz, Stepan & Hruby, Zdenek & Janda, Karel & Kristoufek, Ladislav, 2013. "Provazanost trhu potravin, biopaliv a fosilnich paliv
      [Interconnections within food, biofuel, and fossil fuel markets]
      ," MPRA Paper 43958, University Library of Munich, Germany.
    11. Trujillo-Barrera, Andres & Pennings, Joost M.E., 2013. "Energy and Food Commodity Prices Linkage: An Examination with Mixed-Frequency Data," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 150465, Agricultural and Applied Economics Association.
    12. Sousa, Rita & Aguiar-Conraria, Luís & Soares, Maria Joana, 2014. "Carbon financial markets: A time–frequency analysis of CO2 prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 414(C), pages 118-127.
    13. Zhang, Chuanguo & Qu, Xuqin, 2015. "The effect of global oil price shocks on China's agricultural commodities," Energy Economics, Elsevier, vol. 51(C), pages 354-364.
    14. Pal, Debdatta & Mitra, Subrata K., 2017. "Time-frequency contained co-movement of crude oil and world food prices: A wavelet-based analysis," Energy Economics, Elsevier, vol. 62(C), pages 230-239.
    15. Janda, Karel & Krska, Stepan & Prusa, Jan, 2014. "Odhad nákladů na podporu české fotovoltaické energie
      [The Estimation of the Cost of Promotion of the Czech Photovoltaic Energy]
      ," MPRA Paper 54108, University Library of Munich, Germany.
    16. Han, Liyan & Zhou, Yimin & Yin, Libo, 2015. "Exogenous impacts on the links between energy and agricultural commodity markets," Energy Economics, Elsevier, vol. 49(C), pages 350-358.
    17. Nigatu, Getachew & Adjemian, Michael K., 2016. "The U.S. Role in the Price Determination of Major Agricultural Commodities," 2017 Allied Social Science Association (ASSA) Annual Meeting, January 6-8, 2017, Chicago, Illinois 250119, Agricultural and Applied Economics Association.
    18. repec:eee:finlet:v:21:y:2017:i:c:p:214-221 is not listed on IDEAS
    19. Wang, Yudong & Wu, Chongfeng & Yang, Li, 2014. "Oil price shocks and agricultural commodity prices," Energy Economics, Elsevier, vol. 44(C), pages 22-35.
    20. Ladislav Kristoufek, 2013. "Fractal Markets Hypothesis and the Global Financial Crisis: Wavelet Power Evidence," Papers 1310.1446, arXiv.org.
    21. repec:eee:phsmap:v:490:y:2018:i:c:p:1028-1045 is not listed on IDEAS
    22. repec:eee:eneeco:v:66:y:2017:i:c:p:122-139 is not listed on IDEAS

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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
    • Q16 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - R&D; Agricultural Technology; Biofuels; Agricultural Extension Services
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources

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