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Computational Intelligence in Exchange-Rate Forecasting Author info | Abstract | Publisher info | Download info | Related research | Statistics Andreas S. Andreou (University of Cyprus)
George A. Zombanakis () (Bank of Greece)
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This paper applies computational intelligence methods to exchange rate forecasting. In particular, it employs neural network methodology in order to predict developments of the Euro exchange rate versus the U.S. Dollar and the Japanese Yen. Following a study of our series using traditional as well as specialized, non-parametric methods together with Monte Carlo simulations we employ selected Neural Networks (NNs) trained to forecast rate fluctuations. Despite the fact that the data series have been shown by the Rescaled Range Statistic (R/S) analysis to exhibit random behaviour, their internal dynamics have been successfully captured by certain NN topologies, thus yielding accurate predictions of the two exchange-rate series.
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Paper provided by Bank of Greece in its series Working Papers with number
49.
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Length: 43 pages
Date of creation: Nov 2006Date of revision:
Handle: RePEc:bog:wpaper:49Contact details of provider: Web page: http://www.bankofgreece.gr More information through EDIRC
For technical questions regarding this item, or to correct its listing, contact: (Christina Tsochatzi).
Keywords: Exchange - rate forecasting ; Neural networks ; Other versions of this item:
Find related papers by JEL classification: C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications
This paper has been announced in the following NEP Reports :
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