Computational Intelligence in Exchange-Rate Forecasting
AbstractThis 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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Bibliographic InfoPaper provided by Bank of Greece in its series Working Papers with number 49.
Length: 43 pages
Date of creation: Nov 2006
Date of revision:
Exchange - rate forecasting; Neural networks;
Find related papers by JEL classification:
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
This paper has been announced in the following NEP Reports:
- NEP-ALL-2007-01-14 (All new papers)
- NEP-CBA-2007-01-14 (Central Banking)
- NEP-CMP-2007-01-14 (Computational Economics)
- NEP-ECM-2007-01-14 (Econometrics)
- NEP-ETS-2007-01-14 (Econometric Time Series)
- NEP-FOR-2007-01-14 (Forecasting)
- NEP-IFN-2007-01-14 (International Finance)
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