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Forecasting realized volatility of Chinese crude oil futures with a new secondary decomposition ensemble learning approach

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  • Jiang, Wei
  • Tang, Wanqing
  • Liu, Xiao

Abstract

This study proposes a new VMD-ICEEMDAN-LSTM model, which combines secondary decomposition with long short-term memory neural networks (LSTM) to forecast the realized volatility (RV) of Chinese crude oil futures. The RV sequence is first decomposed into subcomponents and residuals through variational mode decomposition (VMD). Then, the iterative complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is applied to perform a secondary decomposition on the residuals. Finally, we apply LSTM to forecast all decomposed components, and then combine all forecasting results to obtain our final forecast value. Our results show that the VMD-ICEEMDAN-LSTM model significantly outperforms existing individual and combination models.

Suggested Citation

  • Jiang, Wei & Tang, Wanqing & Liu, Xiao, 2023. "Forecasting realized volatility of Chinese crude oil futures with a new secondary decomposition ensemble learning approach," Finance Research Letters, Elsevier, vol. 57(C).
    • Handle: RePEc:eee:finlet:v:57:y:2023:i:c:s1544612323006268
    DOI: 10.1016/j.frl.2023.104254
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    References listed on IDEAS

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    1. Guo, Wei & Liu, Qingfu & Luo, Zhidan & Tse, Yiuman, 2022. "Forecasts for international financial series with VMD algorithms," Journal of Asian Economics, Elsevier, vol. 80(C).
    2. Peter R. Hansen & Asger Lunde & James M. Nason, 2011. "The Model Confidence Set," Econometrica, Econometric Society, vol. 79(2), pages 453-497, March.
    3. Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
    4. He, Feifei & Zhou, Jianzhong & Feng, Zhong-kai & Liu, Guangbiao & Yang, Yuqi, 2019. "A hybrid short-term load forecasting model based on variational mode decomposition and long short-term memory networks considering relevant factors with Bayesian optimization algorithm," Applied Energy, Elsevier, vol. 237(C), pages 103-116.
    5. Linjie Zhan & Zhenpeng Tang & Juan Frausto-Solis, 2022. "Natural Gas Price Forecasting by a New Hybrid Model Combining Quadratic Decomposition Technology and LSTM Model," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-13, December.
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    More about this item

    Keywords

    Crude oil futures; Realized volatility; VMD model; ICEEMDAN model; Secondary decomposition;
    All these keywords.

    JEL classification:

    • B41 - Schools of Economic Thought and Methodology - - Economic Methodology - - - Economic Methodology
    • 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

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