Predicting CO₂ emission prices is an important and challenging task for policy makers and market participants, as carbon prices follow a stochastic process of complex time series with nonstationary and nonlinear characteristics. Existing literature has focused on highly precise point forecasting, but it cannot correctly solve the uncertainties related to carbon price datasets in most cases. This study aims to develop a hybrid forecasting model to estimate in advance the maximum or minimum loss in the stochastic process of CO₂ emission trading price fluctuation. This model can granulate raw data into fuzzy-information granular components with minimum (Low), average (R), and maximum (Up) values as changing space-description parameters. Furthermore, it can forecast carbon prices’ changing space with Low, R, and Up as inputs to support a vector regression. This method’s feasibility and effectiveness is examined using empirical experiments on European Union allowances’ spot and futures prices under the European Union’s Emissions Trading Scheme. The proposed FIG-SVM model exhibits fewer errors and superior performance than ARIMA, ARFIMA, and Markov-switching methods. This study provides several important implications for investors and risk managers involved in trading carbon financial products.

中文翻译：

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二氧化碳排放交易价格波动随机过程中非平稳和非线性时间序列的混合预测模型

对决策者和市场参与者而言，预测CO ₂排放价格是一项重要而具有挑战性的任务，因为碳价遵循具有不稳定和非线性特征的复杂时间序列的随机过程。现有文献集中于高度精确的点预测，但是在大多数情况下，它不能正确解决与碳价数据集有关的不确定性。本研究旨在建立一种混合预测模型，以提前估计CO ₂排放交易价格波动的随机过程中的最大或最小损失。该模型可以将原始数据细化为具有最小（低），平均值（R）和最大值（向上）值作为更改的空间描述参数。此外，它可以使用Low，R和Up作为输入来预测碳价的变化空间，以支持向量回归。该方法的可行性和有效性是通过根据欧盟排放交易计划对欧盟配额的现货和期货价格进行的经验试验来检验的。与ARIMA，ARFIMA和Markov切换方法相比，所提出的FIG-SVM模型表现出更少的错误和更好的性能。这项研究对参与碳金融产品交易的投资者和风险管理者具有重要意义。