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A novel perspective on forecasting non-ferrous metals’ volatility: Integrating deep learning techniques with econometric models

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Listed:
  • Shu, Qi
  • Xiong, Heng
  • Jiang, Wenjun
  • Mamon, Rogemar

Abstract

This study puts forward a new perspective on non-ferrous metals’ volatility prediction in the futures market. Two hybrid deep learning architectures are constructed by embedding assorted convolutional neural networks (CNN) into long short-term memory (LSTM) models, and combining the LSTM networks with various generalized autoregressive conditional heteroscedasticity (GARCH)-type models. We illustrate the numerical implementation of all proposed models on four non-ferrous metal indices. Our findings suggest that the GARCH-LSTM model outperforms other alternatives by examining diverse error metrics. This study marks a significant advancement in the application of integrated deep learning models to enhance the prediction performance of commodity volatility.

Suggested Citation

  • Shu, Qi & Xiong, Heng & Jiang, Wenjun & Mamon, Rogemar, 2023. "A novel perspective on forecasting non-ferrous metals’ volatility: Integrating deep learning techniques with econometric models," Finance Research Letters, Elsevier, vol. 58(PC).
    • Handle: RePEc:eee:finlet:v:58:y:2023:i:pc:s1544612323008541
    DOI: 10.1016/j.frl.2023.104482
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    References listed on IDEAS

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    More about this item

    Keywords

    Volatility; Nonferrous metals; GARCH; Deep learning; Commodity;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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