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Neural Network Pruning Applied to Real Exchange Rate Analysis

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  • Kaashoek, Johan F
  • van Dijk, Herman K

Abstract

Neural networks are fitted to real exchange rates of several industrialized countries. The size and topology of the networks is found through the use of multiple correlation coefficients, principal component analysis of residuals and graphical analysis of network output per hidden layer cell and input layer cell. These pruned neural networks are good approximations to varying non-linear trends in real exchange rates. Non-linear dynamic analysis shows that the long-term equilibrium values of several European currencies correspond to the actual values within the European Monetary System. Based on its long-term equilibrium value, the Euro appears to be undervalued vis-a-vis the US dollar at the introduction of the Euro on 1 January 1999. Copyright © 2002 by John Wiley & Sons, Ltd.

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Bibliographic Info

Article provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.

Volume (Year): 21 (2002)
Issue (Month): 8 (December)
Pages: 559-77

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Handle: RePEc:jof:jforec:v:21:y:2002:i:8:p:559-77

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Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966

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Cited by:
  1. Angela He & Alan Wan, 2009. "Predicting daily highs and lows of exchange rates: a cointegration analysis," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(11), pages 1191-1204.
  2. H.K. van Dijk, 2004. "Twentieth Century Shocks, Trends and Cycles in Industrialized Nations," De Economist, Springer, vol. 152(2), pages 211-232, 06.
  3. Y. Kahiri & A. Shmilovici & S. Hauser, 2006. "Measuring the Efficiency of the Intraday Forex Market with a Universal Data Compression Algorithm," Computing in Economics and Finance 2006 256, Society for Computational Economics.

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