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Multi-step-ahead crude oil price forecasting using a hybrid grey wave model

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Listed:
  • Chen, Yanhui
  • Zhang, Chuan
  • He, Kaijian
  • Zheng, Aibing

Abstract

Crude oil is crucial to the operation and economic well-being of the modern society. Huge changes of crude oil price always cause panics to the global economy. There are many factors influencing crude oil price. Crude oil price prediction is still a difficult research problem widely discussed among researchers. Based on the researches on Heterogeneous Market Hypothesis and the relationship between crude oil price and macroeconomic factors, exchange market, stock market, this paper proposes a hybrid grey wave forecasting model, which combines Random Walk (RW)/ARMA to forecast multi-step-ahead crude oil price. More specifically, we use grey wave forecasting model to model the periodical characteristics of crude oil price and ARMA/RW to simulate the daily random movements. The innovation also comes from using the information of the time series graph to forecast crude oil price, since grey wave forecasting is a graphical prediction method. The empirical results demonstrate that based on the daily data of crude oil price, the hybrid grey wave forecasting model performs well in 15- to 20-step-ahead prediction and it always dominates ARMA and Random Walk in correct direction prediction.

Suggested Citation

  • Chen, Yanhui & Zhang, Chuan & He, Kaijian & Zheng, Aibing, 2018. "Multi-step-ahead crude oil price forecasting using a hybrid grey wave model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 98-110.
    • Handle: RePEc:eee:phsmap:v:501:y:2018:i:c:p:98-110
    DOI: 10.1016/j.physa.2018.02.061
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    2. Li, Ranran & Hu, Yucai & Heng, Jiani & Chen, Xueli, 2021. "A novel multiscale forecasting model for crude oil price time series," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
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    4. Hyeon-Seok Kim & Hui-Sang Kim & Sun-Yong Choi, 2024. "Investigating the Impact of Agricultural, Financial, Economic, and Political Factors on Oil Forward Prices and Volatility: A SHAP Analysis," Energies, MDPI, vol. 17(5), pages 1-24, February.
    5. 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).
    6. Jiang Wu & Feng Miu & Taiyong Li, 2020. "Daily Crude Oil Price Forecasting Based on Improved CEEMDAN, SCA, and RVFL: A Case Study in WTI Oil Market," Energies, MDPI, vol. 13(7), pages 1-20, April.
    7. 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).
    8. Butler, Sunil & Kokoszka, Piotr & Miao, Hong & Shang, Han Lin, 2021. "Neural network prediction of crude oil futures using B-splines," Energy Economics, Elsevier, vol. 94(C).
    9. Wang, Zheng-Xin & Li, Dan-Dan & Zheng, Hong-Hao, 2020. "Model comparison of GM(1,1) and DGM(1,1) based on Monte-Carlo simulation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).
    10. Öztunç Kaymak, Öznur & Kaymak, Yiğit, 2022. "Prediction of crude oil prices in COVID-19 outbreak using real data," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).
    11. Yin, Libo & Wang, Yang, 2019. "Forecasting the oil prices: What is the role of skewness risk?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
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    13. Yingchao Zou & Kaijian He, 2022. "Forecasting Crude Oil Risk Using a Multivariate Multiscale Convolutional Neural Network Model," Mathematics, MDPI, vol. 10(14), pages 1-11, July.

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    More about this item

    Keywords

    Crude oil price forecasting; Grey wave forecasting model; Graphical prediction model; Hybrid model;
    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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q49 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Other

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