School of Financial Technology, Suzhou Industrial Park Institute of Services Outsourcing, Jiangsu, Suzhou City, China.
PLoS One. 2024 Mar 7;19(3):e0294269. doi: 10.1371/journal.pone.0294269. eCollection 2024.
This study aims to investigate the price changes in the carbon trading market and the development of international carbon credits in-depth. To achieve this goal, operational principles of the international carbon credit financing mechanism are considered, and time series models were employed to forecast carbon trading prices. Specifically, an ARIMA(1,1,1)-GARCH(1,1) model, which combines the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and Autoregressive Integrated Moving Average (ARIMA) models, is established. Additionally, a multivariate dynamic regression Autoregressive Integrated Moving Average with Exogenous Inputs (ARIMAX) model is utilized. In tandem with the modeling, a data index system is developed, encompassing various factors that influence carbon market trading prices. The random forest algorithm is then applied for feature selection, effectively identifying features with high scores and eliminating low-score features. The research findings reveal that the ARIMAX Least Absolute Shrinkage and Selection Operator (LASSO) model exhibits high forecasting accuracy for time series data. The model's Mean Squared Error, Root Mean Squared Error, and Mean Absolute Error are reported as 0.022, 0.1344, and 0.1543, respectively, approaching zero and surpassing other evaluation models in predictive accuracy. The goodness of fit for the national carbon market price forecasting model is calculated as 0.9567, indicating that the selected features strongly explain the trading prices of the carbon emission rights market. This study introduces innovation by conducting a comprehensive analysis of multi-dimensional data and leveraging the random forest model to explore non-linear relationships among data. This approach offers a novel solution for investigating the complex relationship between the carbon market and the carbon credit financing mechanism.
本研究旨在深入探讨碳交易市场的价格变化和国际碳信用的发展。为了实现这一目标，考虑了国际碳信用融资机制的运作原则，并采用时间序列模型对碳交易价格进行预测。具体来说，建立了一个结合广义自回归条件异方差（GARCH）和自回归综合移动平均（ARIMA）模型的 ARIMA(1,1,1)-GARCH(1,1)模型。此外，还利用了带有外生输入的多元动态回归自回归综合移动平均（ARIMAX）模型。在建模的同时，开发了一个数据指数系统，涵盖了影响碳市场交易价格的各种因素。然后应用随机森林算法进行特征选择，有效识别高分特征并消除低分特征。研究结果表明，ARIMAX 最小绝对收缩和选择算子（LASSO）模型对时间序列数据具有较高的预测精度。模型的均方误差、均方根误差和平均绝对误差分别报告为 0.022、0.1344 和 0.1543，接近零，在预测精度方面超过其他评估模型。国家碳市场价格预测模型的拟合优度计算为 0.9567，表明所选特征能够很好地解释碳排放权市场的交易价格。本研究通过对多维数据进行综合分析，并利用随机森林模型探索数据之间的非线性关系，引入了创新。这种方法为研究碳市场和碳信用融资机制之间的复杂关系提供了一种新的解决方案。
