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Nonlinear prediction of Malaysian exchange rate with monetary fundamentals

Author

Listed:
  • 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.

Suggested Citation

  • Chun-Teck Lye & Tze-Haw Chan & Chee-Wooi Hooy, 2011. "Nonlinear prediction of Malaysian exchange rate with monetary fundamentals," Economics Bulletin, AccessEcon, vol. 31(3), pages 1960-1967.
    • Handle: RePEc:ebl:ecbull:eb-10-00808
    as

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    References listed on IDEAS

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    Cited by:

    1. Shamaila Butt & Suresh Ramakrishnan & Nanthakumar Loganathan & Muhammad Ali Chohan, 2020. "Evaluating the exchange rate and commodity price nexus in Malaysia: evidence from the threshold cointegration approach," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-19, December.

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    More about this item

    Keywords

    Autoregressive; monetary model; neural network; random walk;
    All these keywords.

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • F3 - International Economics - - International Finance

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