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Correlations between biofuels and related commodities: A taxonomy perspective

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  • Ladislav Kristoufek
  • Karel Janda
  • David zilberman

Abstract

In this paper, we analyze the relationships between the prices of biodiesel, ethanol and related fuels and agricultural commodities with a use of minimal spanning trees and hierarchical trees. We find that in short-term, both ethanol and biodiesel are very weakly connected with the other commodities. In medium-term, the biofuels network becomes more structured. The system splits into two well separated branches - a fuels part and a food part. Biodiesel tends to the fuels branch and ethanol to the food branch. When the periods before and after the food crisis of 2007/2008 are compared, the connections are much stronger for the post-crisis period. This is the first application of this methodology on the biofuels systems.

Suggested Citation

  • Ladislav Kristoufek & Karel Janda & David zilberman, 2012. "Correlations between biofuels and related commodities: A taxonomy perspective," CAMA Working Papers 2012-29, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  • Handle: RePEc:een:camaaa:2012-29
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    File URL: https://cama.crawford.anu.edu.au/sites/default/files/publication/cama_crawford_anu_edu_au/2017-02/29_kristoufek_janda_zilberman_2012.pdf
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    References listed on IDEAS

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    1. Beckman, Jayson & Hertel, Thomas & Tyner, Wallace, 2011. "Validating energy-oriented CGE models," Energy Economics, Elsevier, vol. 33(5), pages 799-806, September.
    2. Busse, Stefan & Brümmer, Bernard & Ihle, Rico, 2010. "Interdependencies between fossil fuel and renewable energy markets: the German biodiesel market," DARE Discussion Papers 1010, Georg-August University of Göttingen, Department of Agricultural Economics and Rural Development (DARE).
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    Cited by:

    1. Vacha, Lukas & Janda, Karel & Kristoufek, Ladislav & Zilberman, David, 2013. "Time–frequency dynamics of biofuel–fuel–food system," Energy Economics, Elsevier, vol. 40(C), pages 233-241.
    2. Nazlioglu, Saban & Erdem, Cumhur & Soytas, Ugur, 2013. "Volatility spillover between oil and agricultural commodity markets," Energy Economics, Elsevier, vol. 36(C), pages 658-665.
    3. Paulus, Michal & Kristoufek, Ladislav, 2015. "Worldwide clustering of the corruption perception," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 428(C), pages 351-358.
    4. Wang, Gang-Jin & Xie, Chi, 2015. "Correlation structure and dynamics of international real estate securities markets: A network perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 424(C), pages 176-193.

    More about this item

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • 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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