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COMPUTATIONAL INTELLIGENCE IN EXCHANGE-RATE FORECASTING

                                                                Andreas S. Andreou
                                                                University of Cyprus
                                                                             and
                                                                George A. Zombanakis
                                                                     Bank of Greece

ABSTRACT

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.

Keywords: Exchange - rate forecasting, Neural networks

JEL classification: C530

Acknowledgements: We are indebted to Heather Gibson and Tassos Anastasatos for their helpful comments. The views expressed in the paper are those of the authors and not necessarily reflect those of the Bank of Greece.

Correspondence:

George. A. Zombanakis,
Economic Research Department,
Bank of Greece, 21 E. Venizelos Ave.,
102 50 Athens, Greece,
Tel.: +30 210 323 5809
Fax: +30 210 323 3025
Email: gzombanakis@bankofgreece.gr


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