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Nonlinear prediction of Malaysian exchange rate with monetary fundamentals

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  • Chun-Teck Lye

    ()
    (Multimedia University)

  • Tze-Haw Chan

    ()
    (Universiti Sains Malaysia)

  • Chee-Wooi Hooy

    ()
    (Universiti Sains Malaysia)

Abstract

This paper compares one-step-ahead out-of-sample predictions on Malaysian Ringgit-US Dollar exchange rate using the generalized regression neural network for a range of forecasting horizons from 1991M3 to 2008M8. We find that the monetary fundamentals are significant in explaining the dynamics of Malaysian exchange rate in a longer forecast horizon as the performance of monetary exchange rate models outperformed the random walk benchmark model. The results also revealed that Malaysian exchange rate market provides profitable short-term arbitrage opportunities with lagged observations, and the integration of autoregressive terms into the monetary exchange rate models enhanced the out-of-sample forecasting performance.

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Bibliographic Info

Article provided by AccessEcon in its journal Economics Bulletin.

Volume (Year): 31 (2011)
Issue (Month): 3 ()
Pages: 1960-1967

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Handle: RePEc:ebl:ecbull:eb-10-00808

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Keywords: Autoregressive; monetary model; neural network; random walk;

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  10. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
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