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Estimation and forecast of carbon emission market volatility based on model averaging method

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  • Wang, Nianling
  • Wang, Qianchao
  • Li, Yong

Abstract

Understanding volatility is essential for risk management and green investment decision-making in the carbon market. However, existing studies lack a unified framework for modeling and estimating carbon market volatility, and predictions are often affected by model uncertainty. Using data from EU emission allowances, we estimate parameters for multiple GARCH models via the Sequential Monte Carlo method and improve forecasting accuracy with model averaging techniques. Our results reveal that carbon market volatility is characterized by spikes, thick tails, asymmetry, and jumps. Based on Model Confidence Set test, model comparison demonstrates that averaged models consistently outperform individual models across various loss criteria. By integrating information from multiple models, the model averaging approach simplifies model selection and plays a pivotal role in supporting volatility timing strategies.

Suggested Citation

  • Wang, Nianling & Wang, Qianchao & Li, Yong, 2025. "Estimation and forecast of carbon emission market volatility based on model averaging method," Economic Modelling, Elsevier, vol. 143(C).
    • Handle: RePEc:eee:ecmode:v:143:y:2025:i:c:s026499932400333x
    DOI: 10.1016/j.econmod.2024.106976
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    More about this item

    Keywords

    Carbon market; Volatility; Model averaging; Sequential Monte Carlo;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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