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摘要 中国近4年才成立的股指期货市场价格呈现出非平稳、非线性的信号特征， 传统的预测方法无法对长相关序列进行精确预测. 将EMD与RBF相结合， 建立了一种新的预测方法对我国股指期货日结算价格进行预测. 结果显示本模型将原本具有长相关性质的原始序列分解为若干个短相关性质的不同频带， 解决了原始序列随机性强， 以及因相邻频带的干扰而造成的系统动力信息反映不足的缺陷； 并与其他预测模型进行比较， 显示出较高的预测精度.
关键词 EMD； RBF神经网络； 股指期货
中图分类号 F830 文献标识码 A
AbstractOnly in the past four years did China set up the stock index futures market displaying the nonstable and nonlinear signal features. The traditional estimation methods cannot make accurate estimation of longrelevant sequence. Combining EMD with RBF， we have created a new method of estimation to predict the daily settlement price for stock index futures. The result shows that this model has separated the original sequence with longrelevance features into several shortrelevance frequency bands， making up for the shortage of system power information caused by the serious randomness of the original sequence and the interruptions from nearby frequency bands. It is also compared with other estimation models to display a relatively high degree of accuracy.
Key wordsEmpirical Mode Decomposition； RBF； stock index futures
1引言
通过价格信号对资源进行合理配置是市场经济条件下经济发展的主要手段. 因此期货市场应运而生， 为国民经济的发展创造了多种风险管理工具， 保证国民经济的运行. FLOROS通过对欧美金融市场的研究， 表明股指期货保证了股市正常健全的发展// Proceedings of the 2nd International Conference on Business Intelligence and Financial Engineering， 2009： 279-282.
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