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"Butterfly Effect" vs Chaos in Energy Futures Markets

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  • Loretta Mastroeni
  • Pierluigi Vellucci

Abstract

In this paper we test for the sensitive dependence on initial conditions (the so called "butterfly effect") of energy futures time series (heating oil, natural gas), and thus the determinism of those series. This paper is distinguished from previous studies in the following points: first, we reread existent works in the literature on energy markets, enlightening the role of \emph{butterfly effect} in chaos definition (introduced by Devaney), using this definition to prevent us from misleading results about ostensible chaoticity of the price series. Second, we test for the time series for sensitive dependence on initial conditions, introducing a coefficient that describes the determinism rate of the series and that represents its reliability level (in percentage). The introduction of this reliability level is motivated by the fact that time series generated from stochastic systems also might show sensitive dependence on initial conditions. According to this perspective, the maximum reliability level obtained here is too low to be able to ensure that there is strong evidence of sensitive The maximum reliability level obtained here was been $\simeq 56\% $, too low to ensure strong evidence of sensitive dependence on initial conditions.

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  • Loretta Mastroeni & Pierluigi Vellucci, 2016. ""Butterfly Effect" vs Chaos in Energy Futures Markets," Papers 1610.05697, arXiv.org.
  • Handle: RePEc:arx:papers:1610.05697
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    1. Apostolos Serletis & Periklis Gogas, 2007. "The North American Natural Gas Liquids Markets are Chaotic," World Scientific Book Chapters, in: Quantitative And Empirical Analysis Of Energy Markets, chapter 17, pages 225-244, World Scientific Publishing Co. Pte. Ltd..
    2. Gradojevic, Nikola & Gencay, Ramazan, 2008. "Overnight interest rates and aggregate market expectations," Economics Letters, Elsevier, vol. 100(1), pages 27-30, July.
    3. Simón Sosvilla-Rivero & Fernando Fernández-Rodriguez & Julián Andrada-Félix, 2005. "Testing chaotic dynamics via Lyapunov exponents," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(7), pages 911-930.
    4. Benedetto, F. & Giunta, G. & Mastroeni, L., 2016. "On the predictability of energy commodity markets by an entropy-based computational method," Energy Economics, Elsevier, vol. 54(C), pages 302-312.
    5. Saeed Moshiri & Faezeh Foroutan, 2006. "Forecasting Nonlinear Crude Oil Futures Prices," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 81-96.
    6. Victor Chwee, 1998. "Chaos in Natural Gas Futures?," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 149-164.
    7. Gençay, Ramazan & Gradojevic, Nikola, 2010. "Crash of '87 -- Was it expected?: Aggregate market fears and long-range dependence," Journal of Empirical Finance, Elsevier, vol. 17(2), pages 270-282, March.
    8. Barkoulas, John T. & Chakraborty, Atreya & Ouandlous, Arav, 2012. "A metric and topological analysis of determinism in the crude oil spot market," Energy Economics, Elsevier, vol. 34(2), pages 584-591.
    9. Adrangi, Bahram & Chatrath, Arjun & Dhanda, Kanwalroop Kathy & Raffiee, Kambiz, 2001. "Chaos in oil prices? Evidence from futures markets," Energy Economics, Elsevier, vol. 23(4), pages 405-425, July.
    10. Mariano Matilla-García, 2007. "Nonlinear Dynamics in Energy Futures," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 7-30.
    11. Kyrtsou, Catherine & Malliaris, Anastasios G. & Serletis, Apostolos, 2009. "Energy sector pricing: On the role of neglected nonlinearity," Energy Economics, Elsevier, vol. 31(3), pages 492-502, May.
    12. Brock, William A. & Sayers, Chera L., 1988. "Is the business cycle characterized by deterministic chaos?," Journal of Monetary Economics, Elsevier, vol. 22(1), pages 71-90, July.
    13. Panas, Epaminondas & Ninni, Vassilia, 2000. "Are oil markets chaotic? A non-linear dynamic analysis," Energy Economics, Elsevier, vol. 22(5), pages 549-568, October.
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