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Testing Non-Linear Dynamics, Long Memory and Chaotic Behaviour of Energy Commodities

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  • Gencer, Murat
  • Unal, Gazanfer

Abstract

This paper contains a set of tests for nonlinearities in energy commodity prices. The tests comprise both standart diagnostic tests for revealing nonlinearities. The latter test procedures make use of models in chaos theory, so-called long-memory models and some asymmetric adjustment models. Empirical tests are carried our with daily data for crude oil, heating oil, gasoline and natural gas time series covering the period 2010-2015. Test result showed that there are strong nonlinearities in the data. The test for chaos, however, is weak or nonexisting. The evidence on long memory (in terms of rescaled range and fractional differencing) is somewhat stronger altough not very compelling.

Suggested Citation

  • Gencer, Murat & Unal, Gazanfer, 2016. "Testing Non-Linear Dynamics, Long Memory and Chaotic Behaviour of Energy Commodities," MPRA Paper 74115, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:74115
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    References listed on IDEAS

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    1. Bask , Mikael, 1997. "Deterministic Chaos in Exchange Rates?," Umeå Economic Studies 453, Umeå University, Department of Economics.
    2. 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.
    3. Samet G nay, 2015. "Chaotic Structure of the BRIC Countries and Turkey's Stock Market," International Journal of Economics and Financial Issues, Econjournals, vol. 5(2), pages 515-522.
    4. Dechert, W D & Gencay, R, 1992. "Lyapunov Exponents as a Nonparametric Diagnostic for Stability Analysis," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 7(S), pages 41-60, Suppl. De.
    5. Chatrath, Arjun & Adrangi, Bahram & Dhanda, Kanwalroop Kathy, 2002. "Are commodity prices chaotic?," Agricultural Economics, Blackwell, vol. 27(2), pages 123-137, August.
    6. 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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    Cited by:

    1. Karasu, Seçkin & Altan, Aytaç, 2022. "Crude oil time series prediction model based on LSTM network with chaotic Henry gas solubility optimization," Energy, Elsevier, vol. 242(C).
    2. Altan, Aytaç & Karasu, Seçkin & Bekiros, Stelios, 2019. "Digital currency forecasting with chaotic meta-heuristic bio-inspired signal processing techniques," Chaos, Solitons & Fractals, Elsevier, vol. 126(C), pages 325-336.
    3. Karasu, Seçkin & Altan, Aytaç & Bekiros, Stelios & Ahmad, Wasim, 2020. "A new forecasting model with wrapper-based feature selection approach using multi-objective optimization technique for chaotic crude oil time series," Energy, Elsevier, vol. 212(C).

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    More about this item

    Keywords

    Energy commodities; Lyapunov exponents; Correlation dimension; chaos; long memory;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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