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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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    Cited by:

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    2. 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 Nov 2020.
    3. Tiwari, Aviral Kumar & Nasreen, Samia & Shahbaz, Muhammad & Hammoudeh, Shawkat, 2020. "Time-frequency causality and connectedness between international prices of energy, food, industry, agriculture and metals," Energy Economics, Elsevier, vol. 85(C).
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    7. 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.
    8. 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.
    9. de Araujo, Fernando Henrique Antunes & Bejan, Lucian & Stosic, Borko & Stosic, Tatijana, 2020. "An analysis of Brazilian agricultural commodities using permutation – information theory quantifiers: The influence of food crisis," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    10. Dániel Fróna & János Szenderák & Mónika Harangi-Rákos, 2019. "The Challenge of Feeding the World," Sustainability, MDPI, vol. 11(20), pages 1-18, October.
    11. 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.
    12. Caporin, Massimiliano & Naeem, Muhammad Abubakr & Arif, Muhammad & Hasan, Mudassar & Vo, Xuan Vinh & Hussain Shahzad, Syed Jawad, 2021. "Asymmetric and time-frequency spillovers among commodities using high-frequency data," Resources Policy, Elsevier, vol. 70(C).
    13. Karel Janda & Ladislav Kristoufek, 2019. "The relationship between fuel and food prices: Methods, outcomes, and lessons for commodity price risk management," CAMA Working Papers 2019-20, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    14. Pereira, Hernane Borges de Barros & Rosário, Raphael Silva do & Pereira, Eder Johnson de Area Leão & Moreira, Davidson Martins & Ferreira, Paulo & Miranda, José Garcia Vivas, 2022. "Network dynamic and stability on European Union," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 587(C).
    15. 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.
    16. Giray GOZGOR & Cahit MEMIS, 2015. "Price volatility spillovers among agricultural commodity and crude oil markets: Evidence from the range-based estimator," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 61(5), pages 214-221.
    17. FRÓNA Dániel, 2020. "Factors Affecting Food Security," Annals of Faculty of Economics, University of Oradea, Faculty of Economics, vol. 1(1), pages 39-49, July.
    18. Lima, Cristiane Rocha Albuquerque & de Melo, Gabriel Rivas & Stosic, Borko & Stosic, Tatijana, 2019. "Cross-correlations between Brazilian biofuel and food market: Ethanol versus sugar," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 687-693.
    19. Ondřej Filip & Karel Janda & Ladislav Krištoufek, 2018. "Ceny biopaliv a souvisejících komodit: analýza s použitím metod minimální kostry grafu a hierarchických stromů [Prices of Biofuels and Related Commodities: an Analysis Using Methods of Minimum Span," Politická ekonomie, Prague University of Economics and Business, vol. 2018(2), pages 218-239.

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    More about this item

    Keywords

    Biofuels; Networks; Causality;
    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
    • 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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