引入SSA的ARIMA-HPSO-Elman组合模型的汇率预测方法 ——基于人民币对美元汇率中间价数据

doi:10.3969/j.issn.0253-2778.2020.04.015

中国科学技术大学学报 ›› 2020, Vol. 50 ›› Issue (4): 516-527.DOI: 10.3969/j.issn.0253-2778.2020.04.015

引入SSA的ARIMA-HPSO-Elman组合模型的汇率预测方法 ——基于人民币对美元汇率中间价数据

杨杰，王相宁

中国科学技术大学管理学院统计与金融系，安徽合肥 230026

收稿日期:2019-05-23 修回日期:2019-08-31 接受日期:2019-08-31 出版日期:2020-04-30 发布日期:2019-08-31
通讯作者: 王相宁
作者简介:杨杰，男，1994年生，硕士.研究方向：国际金融.E-mail: mefirst@mail.ustc.edu.cn

Exchange rate prediction method based on ARIMA-HPSO-Elman combined model with SSA： Based on the central parity rate data of USD/CNY

YANG Jie, WANG Xiangning

Department of Statistics and Finance,School of Management,University of Science and Technology of China, Hefei 230026,China

Received:2019-05-23 Revised:2019-08-31 Accepted:2019-08-31 Online:2020-04-30 Published:2019-08-31

摘要/Abstract

摘要： 汇率兼有线性和非线性的双重混合行为特征，因此单一的线性模型或非线性模型均无法完美地胜任汇率的预测工作.对人民币对美元汇率中间价序列进行研究，首先通过奇异谱分析SSA方法对汇率序列去噪，并对重构后的汇率序列建立ARIMA模型进行拟合预测以提取出原汇率序列的线性成分，其次对残差部分通过基于杂交变异的混合粒子群优化算法优化的Elman神经网络进行建模并预测，两部分结果相加即为原汇率序列的预测值.实证结果表明，人民币汇率波动存在着周期振荡的特征，在汇率序列的样本外30日预测上，基于SSA方法的组合模型预测性能相对单一模型以及未采取SSA方法的组合模型而言，短期的表现均较优.

关键词: 汇率预测, 奇异谱分析, 粒子群优化算法, Elman神经网络

Abstract: Exchange rate has the characteristics of both linear and non-linear mixed behavior. Single linear model and non-linear model are not perfect for forecasting exchange rate.Here the central parity rate series of USD/CNY exchange rate was studied. Firstly, the SSA method was used to denoise the exchange rate series, and ARIMA model was established to fit and predict the reconstructed exchange rate series to extract the linear components of the original exchange rate series. Secondly, the residual part was modeled and predicted by Elman neural network optimized by hybrid particle swarm optimization algorithm based on crossover and mutation. The sum of the results was the predicted value of the original exchange rate series. Empirical results show that CNY exchange rate fluctuation has the characteristics of periodic oscillation. On the 30-day forecast outside the sample of exchange rate series, the performance of the combination model based on SSA method is better than that of the single model and the combination model without SSA method in the short term.

Key words: exchange rate forecasting, singular spectrum analysis, particle swarm optimization, Elman neural network

中图分类号:

F224.9

杨杰，王相宁. 引入SSA的ARIMA-HPSO-Elman组合模型的汇率预测方法 ——基于人民币对美元汇率中间价数据[J]. 中国科学技术大学学报, 2020, 50(4): 516-527.

YANG Jie, WANG Xiangning. Exchange rate prediction method based on ARIMA-HPSO-Elman combined model with SSA： Based on the central parity rate data of USD/CNY[J]. Journal of University of Science and Technology of China, 2020, 50(4): 516-527.

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引入SSA的ARIMA-HPSO-Elman组合模型的汇率预测方法 ——基于人民币对美元汇率中间价数据

Exchange rate prediction method based on ARIMA-HPSO-Elman combined model with SSA： Based on the central parity rate data of USD/CNY

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