Carbon financial futures have both the characteristics of commodity futures and environmental protection and its price is affected by many factors. It is hard and complex for traditional analysis methods to get precise prediction results effectively. How to effectively predict the price trend of carbon financial futures has been focused on by both academia and traders. This study addresses the high prediction error of European allowance (EUA) futures price by constructing a novel approach by combining the support vector machine (SVM) and particle swarm optimization (PSO) algorithm. This article introduces a parameters optimization method, which provides the best parameters for SVM to improve the prediction performance of the EUA futures price. Furthermore, this research uses the realistic trading dataset containing 30,762 EUA futures closing prices to verify the effectiveness and efficiency of the PSO-SVM prediction model. The empirical results show that the prediction performance of the model, especially the radial kernel function, is significantly improved. And this approach can determine the parameters according to the characteristics of the dataset and input the parameters for training and prediction automatically. The PSO-SVM algorithm can effectively predict extreme price fluctuations and overcome the problem of high prediction error caused by parameter constraints.

碳金融期货兼具商品期货和环境保护的特点，其价格受多种因素影响。传统的分析方法很难有效地获得精确的预测结果。学术界和交易商都一直关注如何有效预测碳金融期货的价格趋势。这项研究通过结合支持向量机（SVM）和粒子群优化（PSO）算法构建一种新颖的方法，解决了欧洲配额（EUA）期货价格的高预测误差。本文介绍了一种参数优化方法，该方法为SVM提供了最佳参数，以改善EUA期货价格的预测性能。此外，本研究使用了包含30个，762 EUA期货收盘价验证了PSO-SVM预测模型的有效性和效率。实验结果表明，该模型的预测性能，尤其是径向核函数，得到了显着提高。并且该方法可以根据数据集的特征确定参数，并自动输入用于训练和预测的参数。PSO-SVM算法可以有效地预测价格的极端波动，克服了参数约束导致的预测误差大的问题。并且该方法可以根据数据集的特征确定参数，并自动输入用于训练和预测的参数。PSO-SVM算法可以有效地预测价格的极端波动，克服了参数约束导致的预测误差大的问题。并且该方法可以根据数据集的特征确定参数，并自动输入用于训练和预测的参数。PSO-SVM算法可以有效地预测价格的极端波动，克服了参数约束导致的预测误差大的问题。