Forecasting exchange rates with linear and nonlinear models Online publication date: Wed, 01-Oct-2008
by Rakesh K. Bissoondeeal, Jane M. Binner, Muddun Bhuruth, Alicia Gazely, Veemadevi P. Mootanah
Global Business and Economics Review (GBER), Vol. 10, No. 4, 2008
Abstract: In this paper, the exchange rate forecasting performance of neural network models are evaluated against the random walk, autoregressive moving average and generalised autoregressive conditional heteroskedasticity models. There are no guidelines available that can be used to choose the parameters of neural network models and therefore, the parameters are chosen according to what the researcher considers to be the best. Such an approach, however, implies that the risk of making bad decisions is extremely high, which could explain why in many studies, neural network models do not consistently perform better than their time series counterparts. In this paper, through extensive experimentation, the level of subjectivity in building neural network models is considerably reduced and therefore giving them a better chance of performing well. The results show that in general, neural network models perform better than the traditionally used time series models in forecasting exchange rates.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the Global Business and Economics Review (GBER):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com