Forecasting Malaysian Exchange Rate: Do Artificial Neural Networks Work?
AbstractBeing a small and open economy, the stability and predictability of Malaysian foreign exchange are crucially important. However, despite the general failure of conventional monetary models, foreign exchange misalignments and authority intervention have both caused the forecasting process an uneasy task. The present paper employs the monetary-portfolio balance exchange rate model and its modified version in the analysis. We then compare two Artificial Neural Networks (ANNs) estimation procedures (MLFN and GRNN) with random walk (RW) in the modeling-prediction process of RM/USD during the post-Bretton Wood era (1990M1-2008M8). The out-of-sample forecasting assessment reveals that the ANNs have outperformed the RW, which in particular, the MLFNs outperform GRNNs where as the latter outperform the RW models with consistency in both the exchange rate models by all evaluation criteria. In addition, the findings also show that the modified model has superior forecasting performance than the first model. In brief, economic fundamentals are vital in forecasting and explaining the RM/USD exchange rate. The findings are beneficial in policy making, investment modeling as well as corporate planning.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 26326.
Date of creation: 06 Apr 2010
Date of revision:
Artificial Neural Networks; Forecasting; modified monetary-portfolio balance model; RM/USD;
Find related papers by JEL classification:
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
- F31 - International Economics - - International Finance - - - Foreign Exchange
This paper has been announced in the following NEP Reports:
- NEP-ALL-2010-11-13 (All new papers)
- NEP-CMP-2010-11-13 (Computational Economics)
- NEP-FOR-2010-11-13 (Forecasting)
- NEP-MON-2010-11-13 (Monetary Economics)
- NEP-SEA-2010-11-13 (South East Asia)
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- Kuan, Chung-Ming & Liu, Tung, 1995. "Forecasting Exchange Rates Using Feedforward and Recurrent Neural Networks," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(4), pages 347-64, Oct.-Dec..
- Ahmad Baharumshah & Venus Liew, 2006. "Forecasting Performance of Exponential Smooth Transition Autoregressive Exchange Rate Models," Open Economies Review, Springer, vol. 17(2), pages 235-251, April.
- 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.
- Bruce Mizrach, 1996. "Forecast Comparison in L2," Departmental Working Papers 199524, Rutgers University, Department of Economics.
- S. Baranzoni & P. Bianchi & L. Lambertini, 2000. "Market Structure," Working Papers 368, Dipartimento Scienze Economiche, Universita' di Bologna.
- Chun-Teck Lye Author_Email: firstname.lastname@example.org & Tze-Haw Chan & Chee-Wooi Hooy, 2011.
"Nonlinear Analysis Of Chinese And Malaysian Exchange Rates Predictability With Monetary Fundamentals,"
2nd International Conference on Business and Economic Research (2nd ICBER 2011) Proceeding
2011-270, Conference Master Resources.
- 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.
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