Computational Intelligence in Exchange-Rate Forecasting
Andreas S. Andreou and
George Zombanakis ()
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Andreas S. Andreou: University of Cyprus
No 49, Working Papers from Bank of Greece
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 (search for similar items in EconPapers)
JEL-codes: C53 (search for similar items in EconPapers)
Pages: 43 pages
New Economics Papers: this item is included in nep-cba, nep-cmp, nep-ecm, nep-ets, nep-for and nep-ifn
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Persistent link: https://EconPapers.repec.org/RePEc:bog:wpaper:49
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