Forecasting exchange rates with linear and nonlinear models

Rakesh Bissoondeeal*, Jane M. Binner, Muddun Bharuth, Alicia M. Gazely, Veemadevi P. Mootanah

*Corresponding author for this work

Research output: Contribution to journalArticle

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.

Original languageEnglish
Pages (from-to)414-429
Number of pages16
JournalGlobal Business and Economics Review
Volume10
Issue number4
DOIs
Publication statusPublished - 2008

Keywords

  • ARIMA
  • Autoregressive Integrated Moving Average
  • exchange rates
  • forecasting
  • GARCH
  • Generalised Autoregressive Conditional Heteroskedasticity
  • linear and nonlinear models
  • Neural Networks
  • NNs

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