基于EMD-PSO-SVM误差校正模型的国际碳金融市场价格预测

International Carbon Finance Market Price Prediction Based on EMD-PSO-SVM Error Correction Model

国际碳金融市场价格预测是制定碳金融市场政策和提高风险管理能力的基础。近年来国际碳市场价格呈现出非平稳、非线性等不规律特性,传统应用于社会经济时间序列的统计模型已经越来越难以满足日渐复杂的社会经济系统的需要。基于此本文建立了基于经验模态分解(EMD)-粒子群算法(PSO)-支持向量机(SVM)的国际碳金融市场价格误差校正预测模型。数据选取2008年3月-2013年9月ICE碳排放期货交易所的CER期货(DEC12)和EUA期货(DEC12)的日交易结算价格作为考察样本进行仿真验证。结果显示:①引入EMD方法可以有效解决误差序列随机性强、相邻频带的干扰可能造成误差序列无法体现反映全部系统动力信息的缺陷;②校正后的预测值与误差预测值的趋势具有较高的一致性,预测结果滞后性和拐点误差大的问题得到了很好的解决;③预测结果较其他常用的国际碳金融价格预测模型进行了比较分析,预测精度有了明显提高。本研究提出的预测模型可以为我国针对目前国际碳价格市场所呈现的波动特征下的碳金融市场价格预测提供新的方法和借鉴。

The price prediction of international carbon finance market is the basis for developing carbon finance market policies and improving risk management capabilities. In recent years, the carbon price showing non-stationary and nonlinear irregular features, the traditional time series statistical model used in socio-economic has become difficult to meet the increasingly complex social and economic systems. This paper established an error correction prediction model based on empirical mode decomposition （ EMD）, particle swarm optimization （PSO） and Support Vector Machine （SVM） to predict international carbon finance market price. Then, taking the carbon futures prices of CER and EUA with maturity called DEC 12 respective of Intercontinental Exchange as samples, empirical results show that： ①the introduced EMD method can resolve the deficiencies effectively that error sequences have strong randomness and interference of adjacent band may cause the outcome that the error sequences can not reflect all of the system dynamic information; ②the trend of corrected predicted values and error predicted values has high consistency, and the predict hysteresis and inflection point error can be solved effectively; ③the model has better prediction precision after comparing to other models commonly used in international carbon finance price prediction. This study proposes a model used in carbon price prediction to provide a new method and reference under the fluctuation characteristics of current international carbon market price.