# 15-125/III (2015-11-06)

David E. Allen, The University of Sydney, The University of South Australia, Australia; Michael McAleer, National Tsing Hua University, Taiwan; Erasmus University Rotterdam, the Netherlands; Complutense University of Madrid, Spain; Shelton Peiris, The University of Sydney, Australia; Abhay K. Singh, Edith Cowan University, Australia
Non linear models, time series, non-parametric, smooth-transition regression models, neural networks, GMDH shell
JEL codes:
C45, C53, F3, G15

This paper features an analysis of major currency exchange rate movements in relation to the US dollar, as constituted in US dollar terms. Euro, British pound, Chinese yuan, and Japanese yen are modelled using a variety of non-linear models, including smooth transition regression models, logistic smooth transition regressions models, threshold autoregressive models, nonlinear autoregressive models, and additive nonlinear autoregressive models, plus Neural
Network models.The results suggest that there is no dominating class of time series models, and the different currency pairs relationships with the US dollar are captured best by neural net regression models, over the ten year sample of daily exchange rate returns data, from August 2005 to August 2015.