IDEAS home Printed from
   My bibliography  Save this article

Nonlinear Analysis Of Chinese And Malaysian Exchange Rates Predictability With Monetary Fundamentals


  • Chun-Teck Lye

    () (Centre for Foundation Studies & Extension Education, Multimedia University)

  • Tze-Haw Chan

    () (School of Management, Universiti Sains Malaysia)

  • Chee-Wooi Hooy

    () (School of Management, Universiti Sains Malaysia)


The Chinese Renminbi (RMB) and Malaysian Ringgit (MYR) are pegged to US Dollar during the 1997-98 Asian financial crisis and continued up until the China and Malaysia de-pegged their currencies and announced a new exchange rate regime at the same day on 21st July 2005. By focusing on the post-July 2005 period (August 2005 to July 2010), this paper study the predictability of monthly RMB/USD and MYR/USD exchange rates in different forecast horizons using the generalized regression neural network (GRNN) with discrete and relative monetary fundamentals. Based on a random validation set, the optimal smoothing parameter in the GRNN is attained and subsequently utilized in the optimal GRNN forecasting model for one-step-ahead out-of-sample predictions. The results of the empirical study disclosed that the discrete monetary fundamentals are more informative in the Chinese and Malaysian exchange rates forecasting as compared to the relative monetary fundamentals. Nevertheless, seeing that the overall forecasting performance of the GRNN forecasting models underperformed the random walk benchmark model, the findings revealed that both discrete and relative monetary fundamentals failed to explain the dynamics of Chinese and Malaysian exchange rates except for the case of Chinese Renminbi vis-à-vis the US Dollar in the 12-month forecast horizon

Suggested Citation

  • Chun-Teck Lye & Tze-Haw Chan & Chee-Wooi Hooy, 2012. "Nonlinear Analysis Of Chinese And Malaysian Exchange Rates Predictability With Monetary Fundamentals," Journal of Global Business and Economics, Global Research Agency, vol. 5(1), pages 38-49, July.
  • Handle: RePEc:grg:01biss:v:5:y:2012:i:1:p:38-49

    Download full text from publisher

    File URL:
    Download Restriction: no

    File URL:
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    1. Panda, Chakradhara & Narasimhan, V., 2007. "Forecasting exchange rate better with artificial neural network," Journal of Policy Modeling, Elsevier, vol. 29(2), pages 227-236.
    2. Rapach, David E. & Wohar, Mark E., 2002. "Testing the monetary model of exchange rate determination: new evidence from a century of data," Journal of International Economics, Elsevier, vol. 58(2), pages 359-385, December.
    3. Huang, Haizhou & Wang, Shuilin, 2004. "Exchange rate regimes: China's experience and choices," China Economic Review, Elsevier, vol. 15(3), pages 336-342.
    4. Hooper, Peter & Morton, John, 1982. "Fluctuations in the dollar: A model of nominal and real exchange rate determination," Journal of International Money and Finance, Elsevier, vol. 1(1), pages 39-56, January.
    5. Meese, Richard A. & Rogoff, Kenneth, 1983. "Empirical exchange rate models of the seventies : Do they fit out of sample?," Journal of International Economics, Elsevier, vol. 14(1-2), pages 3-24, February.
    6. Rakesh K. Bissoondeeal & Jane M. Binner & Muddun Bhuruth & Alicia Gazely & Veemadevi P. Mootanah, 2008. "Forecasting exchange rates with linear and nonlinear models," Global Business and Economics Review, Inderscience Enterprises Ltd, vol. 10(4), pages 414-429.
    7. 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.
    8. Soofi, Abdol S. & Cao, Liangyue, 1999. "Nonlinear deterministic forecasting of daily Peseta-Dollar exchange rate," Economics Letters, Elsevier, vol. 62(2), pages 175-180, February.
    9. Chan, Tze-Haw & Lye, Chun Teck & Hooy, Chee-Wooi, 2010. "Forecasting Malaysian Exchange Rate: Do Artificial Neural Networks Work?," MPRA Paper 26326, University Library of Munich, Germany.
    10. Joseph Plasmans & William Verkooijen & Hennie Daniels, 1998. "Estimating structural exchange rate models by artificial neural networks," Applied Financial Economics, Taylor & Francis Journals, vol. 8(5), pages 541-551.
    11. Azad, A.S.M. Sohel, 2009. "Random walk and efficiency tests in the Asia-Pacific foreign exchange markets: Evidence from the post-Asian currency crisis data," Research in International Business and Finance, Elsevier, vol. 23(3), pages 322-338, September.
    Full references (including those not matched with items on IDEAS)

    More about this item


    GRNN; Neural network; Random walk; Renminbi; Ringgit;

    JEL classification:

    • M0 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - General


    Access and download statistics


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:grg:01biss:v:5:y:2012:i:1:p:38-49. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (editor). General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.