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Volatility forecasting of clean energy ETF using GARCH-MIDAS with neural network model

Author

Listed:
  • Zhang, Li
  • Wang, Lu
  • Nguyen, Thong Trung
  • Ren, Ruiyi

Abstract

This paper utilizes a hybrid model to analyze the impression of information from the GECON indicator on the volatility prediction of the clean energy market. The model architecture is constructed by embedding a recurrent neural network (RNN) into the GARCH-MIDAS model. The results show that RNN-GARCH-MIDAS-GECON achieves optimal ranking in volatility prediction. This work confirms the advantages of embedded hybrid integrated models in capturing nonlinear information in financial markets and achieving significant progress in volatility forecasts. Notably, this research will help to promote the construction of clean energy development and energy transition pathways.

Suggested Citation

  • Zhang, Li & Wang, Lu & Nguyen, Thong Trung & Ren, Ruiyi, 2024. "Volatility forecasting of clean energy ETF using GARCH-MIDAS with neural network model," Finance Research Letters, Elsevier, vol. 70(C).
    • Handle: RePEc:eee:finlet:v:70:y:2024:i:c:s1544612324013151
    DOI: 10.1016/j.frl.2024.106286
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    More about this item

    Keywords

    Clean energy ETF; Recurrent neural network; GARCH-MIDAS; Volatility forecasting;
    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
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • Q02 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Commodity Market
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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