Title: Forecasting exchange rates with linear and nonlinear models

Authors: Rakesh K. Bissoondeeal, Jane M. Binner, Muddun Bhuruth, Alicia Gazely, Veemadevi P. Mootanah

Addresses: Economics and Strategy Group, Aston Business School, Aston University, Aston Triangle, Birmingham B4 7ET, UK. ' Economics and Strategy Group, Aston Business School, Aston University, Aston Triangle, Birmingham B4 7ET, UK. ' Department of Mathematics, University of Mauritius, Reduit, Mauritius. ' Department of Information Management and Systems, Nottingham Business School, Nottingham Trent University, Nottingham NG1 4BU, UK. ' Department of Mathematics, University of Mauritius, Reduit, Mauritius

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.

DOI: 10.1504/GBER.2008.020593

Global Business and Economics Review, 2008 Vol.10 No.4, pp.414 - 429

Published online: 01 Oct 2008 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article