Forecasting exchange rates with linear and nonlinear models

Rakesh Bissoondeeal, Jane M. Binner, Muddun Bhuruth, Alicia Gazely and Veemadevi P. Mootanah

Global Business and Economics Review, 2008, vol. 10, issue 4, 414-429

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.

Keywords: exchange rates; forecasting; linear models; nonlinear models; autoregressive integrated moving average; ARIMA models; neural networks; ANNs; generalised autoregressive conditional heteroskedasticity; GARCH models; random walk models. (search for similar items in EconPapers)
Date: 2008
References: Add references at CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://www.inderscience.com/link.php?id=20593 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:gbusec:v:10:y:2008:i:4:p:414-429

Access Statistics for this article

More articles in Global Business and Economics Review from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().