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Enhancing the predictability of crude oil markets with hybrid wavelet approaches

Author

Listed:
  • Uddin, Gazi Salah
  • Gençay, Ramazan
  • Bekiros, Stelios
  • Sahamkhadam, Maziar

Abstract

We explore the robustness, efficiency and accuracy of the multi-scale forecasting in crude oil markets. We adopt a novel hybrid wavelet auto-ARMA model to detect the inherent nonlinear dynamics of crude oil returns with an explicitly defined hierarchical structure. Entropic estimation is employed to calculate the optimal level of the decomposition. The wavelet-based forecasting method accounts for the chaotic behavior of oil series, whilst captures drifts, spikes and other non-stationary effects which common frequency-domain methods miss out completely. These results shed new light upon the predictability of crude oil markets in nonstationary settings.

Suggested Citation

  • Uddin, Gazi Salah & Gençay, Ramazan & Bekiros, Stelios & Sahamkhadam, Maziar, 2019. "Enhancing the predictability of crude oil markets with hybrid wavelet approaches," Economics Letters, Elsevier, vol. 182(C), pages 50-54.
    • Handle: RePEc:eee:ecolet:v:182:y:2019:i:c:p:50-54
    DOI: 10.1016/j.econlet.2019.05.041
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    Citations

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    Cited by:

    1. Uddin, Gazi Salah & Tang, Ou & Sahamkhadam, Maziar & Taghizadeh-Hesary, Farhad & Yahya, Muhammad & Cerin, Pontus & Rehme, Jakob, 2021. "Analysis of Forecasting Models in an Electricity Market under Volatility," ADBI Working Papers 1212, Asian Development Bank Institute.
    2. Long, Shaobo & Guo, Jiaqi, 2022. "Infectious disease equity market volatility, geopolitical risk, speculation, and commodity returns: Comparative analysis of five epidemic outbreaks," Research in International Business and Finance, Elsevier, vol. 62(C).
    3. Christos Floros & Georgios Galyfianakis, 2020. "Bubbles in Crude Oil and Commodity Energy Index: New Evidence," Energies, MDPI, vol. 13(24), pages 1-11, December.
    4. Wen, Danyan & Liu, Li & Wang, Yudong & Zhang, Yaojie, 2022. "Forecasting crude oil market returns: Enhanced moving average technical indicators," Resources Policy, Elsevier, vol. 76(C).
    5. Mustanen, Dmitri & Maaitah, Ahmad & Mishra, Tapas & Parhi, Mamata, 2022. "The power of investors’ optimism and pessimism in oil market forecasting," Energy Economics, Elsevier, vol. 114(C).
    6. Manickavasagam, Jeevananthan & Visalakshmi, S. & Apergis, Nicholas, 2020. "A novel hybrid approach to forecast crude oil futures using intraday data," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    7. Shahzad, Umer & Jena, Sangram Keshari & Tiwari, Aviral Kumar & Doğan, Buhari & Magazzino, Cosimo, 2022. "Time-frequency analysis between Bloomberg Commodity Index (BCOM) and WTI crude oil prices," Resources Policy, Elsevier, vol. 78(C).

    More about this item

    Keywords

    Wavelet decomposition; Forecasting; Crude oil;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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