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Forecasting carbon prices with high-frequency data and irregular shocks: A hybrid P-NN-MIDAS framework

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  • Zhuo, Xingxuan
  • Chen, Zijiao
  • Zhang, Fangyun

Abstract

While carbon pricing is a critical indicator of market efficiency, existing research largely relies on low-frequency macroeconomic data, neglecting the real-time impact of ultra-high-frequency drivers. This paper addresses this gap by examining the influence of intraday weather, electricity, and financial uncertainty on daily carbon price dynamics. To achieve this, this study proposes a novel hybrid model integrating Prophet, Neural Networks, and Mixed Data Sampling (MIDAS) to fuse mixed-frequency time series with irregular event data. Applied to the EU ETS, the model significantly outperforms traditional benchmarks. These results highlight the critical role of high-frequency dynamics in carbon markets and offer precise tools for policy optimization.

Suggested Citation

  • Zhuo, Xingxuan & Chen, Zijiao & Zhang, Fangyun, 2026. "Forecasting carbon prices with high-frequency data and irregular shocks: A hybrid P-NN-MIDAS framework," Finance Research Letters, Elsevier, vol. 97(C).
    • Handle: RePEc:eee:finlet:v:97:y:2026:i:c:s1544612326003582
    DOI: 10.1016/j.frl.2026.109828
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