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On the predictability of crude oil market: A hybrid multiscale wavelet approach

Author

Listed:
  • Stelios Bekiros

    (EUI - European University Institute - Institut Universitaire Européen)

  • Jose Arreola Hernandez

    (Rennes SB - Rennes School of Business)

  • Gazi Salah Uddin

    (LIU - Linköping University)

  • Ahmed Taneem Muzaffar

    (International Labour Organization)

Abstract

Past research indicates that forecasting is important in understanding price dynamics across assets. We explore the potentiality of multiscale forecasting in the crude oil market by employing a wavelet multiscale analysis on returns and volatilities of Brent and West Texas Intermediate crude oil indices between January 1, 2001, and May 1, 2015. The analysis is based on a shift-invariant discrete wavelet transform, augmented by an entropy-based methodology for determining the optimal timescale decomposition under different market regimes. The empirical results show that the five-step-ahead wavelet forecast that is based on volatilities outperforms the random walk forecast, relative to the wavelet forecast that is based on returns. Optimal wavelet causality forecasting for returns is suggested across all frequencies (i.e., daily–yearly), whereas for volatilities it is suggested only up to quarterly frequencies. These results may have important implications for market efficiency and predictability of prices on the crude oil markets.

Suggested Citation

  • Stelios Bekiros & Jose Arreola Hernandez & Gazi Salah Uddin & Ahmed Taneem Muzaffar, 2020. "On the predictability of crude oil market: A hybrid multiscale wavelet approach," Post-Print hal-02956380, HAL.
    • Handle: RePEc:hal:journl:hal-02956380
    DOI: 10.1002/for.2635
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    Cited by:

    1. 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).
    2. 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).
    3. Zhang, Junting & Liu, Haifei & Bai, Wei & Li, Xiaojing, 2024. "A hybrid approach of wavelet transform, ARIMA and LSTM model for the share price index futures forecasting," The North American Journal of Economics and Finance, Elsevier, vol. 69(PB).
    4. Li, Chuchu & Lin, Qin & Huang, Dong & Grifoll, Manel & Yang, Dong & Feng, Hongxiang, 2023. "Is entropy an indicator of port traffic predictability? The evidence from Chinese ports," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 612(C).
    5. Christos Floros & Georgios Galyfianakis, 2020. "Bubbles in Crude Oil and Commodity Energy Index: New Evidence," Energies, MDPI, vol. 13(24), pages 1-11, December.
    6. Jiaying Peng & Zhenghui Li & Benjamin M. Drakeford, 2020. "Dynamic Characteristics of Crude Oil Price Fluctuation—From the Perspective of Crude Oil Price Influence Mechanism," Energies, MDPI, vol. 13(17), pages 1-19, August.
    7. Lu, Botao & Ma, Feng & Wang, Jiqian & Ding, Hui & Wahab, M.I.M., 2021. "Harnessing the decomposed realized measures for volatility forecasting: Evidence from the US stock market," International Review of Economics & Finance, Elsevier, vol. 72(C), pages 672-689.

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