美元/欧元汇率的趋势与波动分析及区间预测  

Trends and Volatility Analysis and the Interval Forecast of USD/EUR Exchange Rate

作　　者：陈静[1] 李星野[1] CHEN Jing;LI Xingye(Business School,University of Shanghai for Science and Technology,Shanghai 200093,China)

机构地区：[1]上海理工大学管理学院

出　　处：《上海理工大学学报》2019年第2期196-204,共9页Journal of University of Shanghai For Science and Technology

摘　　要：对美元/欧元汇率进行趋势与波动分析并作出区间预测。利用BP神经网络提取趋势,对残差分别运用自回归移动平均模型和广义自回归条件异方差模型分析波动性,将趋势与波动性结合给出区间预测。对2001年7月至2017年10月美元/欧元汇率的研究发现,BP神经网络具有很好的非线性刻画能力,但只有合适的预测精度才能得出较好的预测区间,同时也发现,广义自回归条件异方差模型对波动性的分析效果优于自回归移动平均模型。因此,BP神经网络模型与广义自回归条件异方差模型的组合模型(BP-GARCH模型)更适合时间序列的中长期区间预测。通过调节BP神经网络的参数、误差及预测精度提高组合模型的精度。Analyzing the trend and fluctuation of time series and making the interval forecast of USD/EUR exchange rate.It is of great value to improve the current accuracy of the forecasting method based on the trend.A BP neural network was used to extract the trend and the volatility was analyzed by using a auto-regressive moving average model and a generalized auto-regressive conditional heteroscedasticity model.Finally,the trend and volatility were combined to give the forecast.By the study of the USD/EUR exchange rate from July 2001 to October 2017,it is found that the BP neural network has a good non-linear characterization ability,but only the appropriate prediction accuracy can lead to a better prediction interval and the regression conditional heteroscedasticity model is superior to the auto-regressive moving average model for the analysis of volatility.The accuracy of the combined model can be improved by adjusting the parameters,errors and prediction accuracy of the BP neural network.

关 键 词：BP神经网络 GARCH模型 组合模型 区间预测 

分 类 号：F832.6[经济管理—金融学]