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Carbon option price forecasting based on modified fractional Brownian motion optimized by GARCH model in carbon emission trading

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  • Liu, Zhibin
  • Huang, Shan

Abstract

With the rapid growth of carbon trading, the development of carbon financial derivatives such as carbon options has become inevitable. This paper established a model based on GARCH and fractional Brownian motion (FBM), hoping to provide reference for China's upcoming carbon option trading through carbon option price forecasting research. The fractal characteristic of carbon option prices indicates that it is reasonable to use FBM to predict option prices. The GARCH model can make up for the lack of fixed FBM volatility. In this paper, the daily closing prices of EUA option contracts on the European Energy Exchange are selected as samples for price prediction. The GARCH model was used to determine the return volatility, and then the FBM was used to calculate the forecast price for the next 60 days. The results showed that the predicted price can better fit the actual price. This paper further compares the price prediction results of this model with the other three models through line graphs and error evaluation indicators such as MAPE, MAE and MSE. It is confirmed that the prediction results of the model in this paper is the closest to the actual price.

Suggested Citation

  • Liu, Zhibin & Huang, Shan, 2021. "Carbon option price forecasting based on modified fractional Brownian motion optimized by GARCH model in carbon emission trading," The North American Journal of Economics and Finance, Elsevier, vol. 55(C).
    • Handle: RePEc:eee:ecofin:v:55:y:2021:i:c:s1062940820301959
    DOI: 10.1016/j.najef.2020.101307
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    Cited by:

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    2. Qing Liu & Huina Jin & Xiang Bai & Jinliang Zhang, 2023. "Prediction and Analysis of the Price of Carbon Emission Rights in Shanghai: Under the Background of COVID-19 and the Russia–Ukraine Conflict," Mathematics, MDPI, vol. 11(14), pages 1-16, July.
    3. Hu, Yuanfeng & Tian, Yixiang, 2024. "The role of green reputation, carbon trading and government intervention in determining the green bond pricing: An externality perspective," International Review of Economics & Finance, Elsevier, vol. 89(PB), pages 46-62.
    4. Zhong, Meirui & Zhang, Rui & Ren, Xiaohang, 2023. "The time-varying effects of liquidity and market efficiency of the European Union carbon market: Evidence from the TVP-SVAR-SV approach," Energy Economics, Elsevier, vol. 123(C).
    5. Chao Zhang & Yihang Zhao & Huiru Zhao, 2022. "A Novel Hybrid Price Prediction Model for Multimodal Carbon Emission Trading Market Based on CEEMDAN Algorithm and Window-Based XGBoost Approach," Mathematics, MDPI, vol. 10(21), pages 1-16, November.
    6. Zhang, Junting & Liu, Haifei & Bai, Wei & Li, Xiaojing, 2024. "A hybrid approach of wavelet transform, ARIMA and LSTM model for the share price index futures forecasting," The North American Journal of Economics and Finance, Elsevier, vol. 69(PB).
    7. Liu, Jiatong & Mao, Weifang & Qiao, Xingzhi, 2023. "Dynamic and asymmetric effects between carbon emission trading, financial uncertainties, and Chinese industry stocks: Evidence from quantile-on-quantile and causality-in-quantiles analysis," The North American Journal of Economics and Finance, Elsevier, vol. 65(C).
    8. Zhang, Fang & Xia, Yan, 2022. "Carbon price prediction models based on online news information analytics," Finance Research Letters, Elsevier, vol. 46(PA).
    9. Yumin Li & Ruiqi Yang & Xiaoman Wang & Jiaming Zhu & Nan Song, 2023. "Carbon Price Combination Forecasting Model Based on Lasso Regression and Optimal Integration," Sustainability, MDPI, vol. 15(12), pages 1-26, June.
    10. Jiaojiao Sun & Feng Dong, 2023. "Optimal reduction and equilibrium carbon allowance price for the thermal power industry under China’s peak carbon emissions target," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-27, December.
    11. Fansheng Meng & Rong Dou, 2024. "Prophet-LSTM-BP Ensemble Carbon Trading Price Prediction Model," Computational Economics, Springer;Society for Computational Economics, vol. 63(5), pages 1805-1825, May.

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