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

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Author Info

  • Chun-Teck Lye Author_Email: ctlye@mmu.edu.my

    (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)

Abstract

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

(This abstract was borrowed from another version of this item.)

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Bibliographic Info

Paper provided by Conference Master Resources in its series 2nd International Conference on Business and Economic Research (2nd ICBER 2011) Proceeding with number 2011-270.

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Date of creation: Mar 2011
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Publication status: Published in 2nd ICBER 2011 Proceeding, March 2011
Handle: RePEc:cms:2icb11:2011-270

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Web page: http://www.internationalconference.com.my/proceeding.htm

Related research

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

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References

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  1. Huang, Haizhou & Wang, Shuilin, 2004. "Exchange rate regimes: China's experience and choices," China Economic Review, Elsevier, vol. 15(3), pages 336-342.
  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. 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.
  4. 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.
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  6. 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.
  7. 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.
  8. 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.
  9. 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.
  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.
  11. Panda, Chakradhara & Narasimhan, V., 2007. "Forecasting exchange rate better with artificial neural network," Journal of Policy Modeling, Elsevier, vol. 29(2), pages 227-236.
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