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Interpretable EU ETS Phase 4 prices forecasting based on deep generative data augmentation approach

Author

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  • Liu, Dinggao
  • Chen, Kaijie
  • Cai, Yi
  • Tang, Zhenpeng

Abstract

This paper proposes an interpretable deep learning method based on generative data augmentation for forecasting carbon allowance prices in the EU Emissions Trading System (ETS) Phase 4. Utilizing TimeGAN, we generate near-real expanded data to enhance the training sets. Temporal Fusion Transformer (TFT) is used to quantify the contribution of impact factors. The results show that the augmentation effectively improved the prediction accuracy. Interpretability analysis reveals that Brent crude oil, NBP natural gas, and Rotterdam coal are the top three contributors. Our findings offer a strong approach for the new phase price forecasting, helping market participants and policymakers in informed decision-making.

Suggested Citation

  • Liu, Dinggao & Chen, Kaijie & Cai, Yi & Tang, Zhenpeng, 2024. "Interpretable EU ETS Phase 4 prices forecasting based on deep generative data augmentation approach," Finance Research Letters, Elsevier, vol. 61(C).
    • Handle: RePEc:eee:finlet:v:61:y:2024:i:c:s1544612324000680
    DOI: 10.1016/j.frl.2024.105038
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    More about this item

    Keywords

    Carbon prices; Data augmentation; Multivariate time series; Interpretability; TimeGAN; Temporal Fusion Transformer;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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