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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This research was supported by a grant from Payame Noor University, Tehran-Iran.
This paper was recommended for publication by Editor WANG Shouyang
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Beneki, C., Yarmohammadi, M. Forecasting exchange rates: An optimal approach. J Syst Sci Complex 27, 21–28 (2014). https://doi.org/10.1007/s11424-014-3304-5
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DOI: https://doi.org/10.1007/s11424-014-3304-5