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Regime-Dependent Topological Properties of Biofuels Networks

Author

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  • Ladislav Kristoufek
  • Karel Janda
  • David Zilberman

Abstract

We analyze the relationships between biodiesel, ethanol and related fuels and agricultural commodities with a use of minimal spanning trees and hierarchical trees and Granger causality. We construct this trees for different frequencies (weekly, monthly and quarterly). We find that in short-term, both ethanol and biodiesel are very weakly connected with the other commodities. In medium and long term, the network structure becomes more interesting. We especially concentrate on the links and comovements between the commodities for different stages of the market based on the level of prices of important commodities or groups of commodities. Such approach in general confirms the separation of network into fuels and food branches for majority of analyzed cases.

Suggested Citation

  • Ladislav Kristoufek & Karel Janda & David Zilberman, 2012. "Regime-Dependent Topological Properties of Biofuels Networks," CAMA Working Papers 2012-49, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  • Handle: RePEc:een:camaaa:2012-49
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    File URL: https://cama.crawford.anu.edu.au/sites/default/files/publication/cama_crawford_anu_edu_au/2017-03/49_kristoufek_janda_zilberman_2012.pdf
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Lahmiri, Salim, 2016. "Clustering of Casablanca stock market based on hurst exponent estimates," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 310-318.
    2. 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.
    3. Gautier Marti & Frank Nielsen & Miko{l}aj Bi'nkowski & Philippe Donnat, 2017. "A review of two decades of correlations, hierarchies, networks and clustering in financial markets," Papers 1703.00485, arXiv.org, revised May 2018.
    4. 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.
    5. 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.
    6. 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.
    7. repec:prg:jnlpol:v:2018:y:2018:i:2:id:1185:p:218-239 is not listed on IDEAS

    More about this item

    Keywords

    Biofuels; Networks; Causality;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources

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