| FORECASTING ：EXCHANGE RATES： AN OPTIMAL APPROACH |
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| 引用本文： | BENEKI Christina,YARMOHAMMADI Masoud. FORECASTING ：EXCHANGE RATES： AN OPTIMAL APPROACH[J]. 系统科学与复杂性, 2014, 27(1): 21-28. DOI: 10.1007/s11424-014-3304-5 |
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| 作者姓名： | BENEKI Christina YARMOHAMMADI Masoud |
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| 作者单位： | [1]School of Business and Economics, Department of Business Administration, Technological Educational Instituteof Ionian Islands, 31100 Lefkada, Greece. [2]Department of Statistics, Payame Noor University, 19395-4697 Tehran, Islamic Republic of Iran. |
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| 基金项目： | This research was supported by a grant from Payame Noor University, Tehran-lran. |
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| 摘 要： | This paper looks at forecasting daily exchange rates for the United Kingdom, European Union, and China. Here, the authors evaluate the forecasting performance of neural networks （NN）, vector singular spectrum analysis （VSSA）, and recurrent singular spectrum analysis （RSSA） for fore casting exchange rates in these countries. The authors find statistically significant evidence based on the RMSE, that both VSSA and RSSA models outperform NN at forecasting the highly unpredictable exchange rates for China. However, the authors find no evidence to suggest any difference between the forecasting accuracy of the three models for UK and EU exchange rates.
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| 关 键 词： | 预测性能 汇率 奇异谱分析 理想 神经网络 误差统计 作者 均方根 |
Forecasting exchange rates: An optimal approach |
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| Christina Beneki,Masoud Yarmohammadi. Forecasting exchange rates: An optimal approach[J]. Journal of Systems Science and Complexity, 2014, 27(1): 21-28. DOI: 10.1007/s11424-014-3304-5 |
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| Authors: | Christina Beneki Masoud Yarmohammadi |
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| Affiliation: | 1. School of Business and Economics, Department of Business Administration, Technological Educational Institute of Ionian Islands, 31100, Lefkada, Greece
2. Department of Statistics, Payame Noor University, 19395-4697, Tehran, Islamic Republic of Iran
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| Abstract: | This paper looks at forecasting daily exchange rates for the United Kingdom, European Union, and China. Here, the authors evaluate the forecasting performance of neural networks (NN), vector singular spectrum analysis (VSSA), and recurrent singular spectrum analysis (RSSA) for forecasting exchange rates in these countries. The authors find statistically significant evidence based on the RMSE, that both VSSA and RSSA models outperform NN at forecasting the highly unpredictable exchange rates for China. However, the authors find no evidence to suggest any difference between the forecasting accuracy of the three models for UK and EU exchange rates. |
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| Keywords: | China European union exchange rates forecasting neural networks recurrent singularspectrum analysis United Kingdom vector singular spectrum analysis. |
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