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China’s carbon emission allowance prices forecasting and option designing in uncertain environment

Author

Listed:
  • Lifen Jia

    (Capital University of Economics and Business)

  • Linya Zhang

    (Capital University of Economics and Business)

  • Wei Chen

    (Capital University of Economics and Business)

Abstract

Carbon emissions trading is pivotal for advancing China’s low-carbon goals. As the primary tradable asset in the carbon market, carbon emission allowances inevitably experience price fluctuations. However, numerous empirical studies show that the frequency of real-world data is highly unstable, which results in the failure of probabilistic modeling. Therefore, this paper aims to model the dynamics of carbon emission allowance prices in China using four mainstream uncertain differential equations. The optimal model is chosen through rolling window cross-validation using the criterion of minimizing average testing errors. Parameters of the optimal model are determined by moment estimation based on residuals, and the model’s effectiveness is also assessed through uncertain two-sided hypothesis testing. Additionally, we forecast carbon emission allowance prices and their 95% confidence intervals for the next 14 business days. To manage trading risks, we propose a customized carbon option contract for pricing European carbon options and conduct sensitivity analysis on key parameters. Finally, we present a paradox of stochastic differential equations for modeling carbon emission allowance prices.

Suggested Citation

  • Lifen Jia & Linya Zhang & Wei Chen, 2024. "China’s carbon emission allowance prices forecasting and option designing in uncertain environment," Fuzzy Optimization and Decision Making, Springer, vol. 23(4), pages 539-560, December.
    • Handle: RePEc:spr:fuzodm:v:23:y:2024:i:4:d:10.1007_s10700-024-09432-y
    DOI: 10.1007/s10700-024-09432-y
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    References listed on IDEAS

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