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Nonlinear Time Series and Neural-Network Models of Exchange Rates between the US Dollar and Major Currencies

David Allen, Michael McAleer, Shelton Peiris and Abhay K. Singh
Additional contact information
Shelton Peiris: School of Mathematics and Statistics, the University of Sydney, Sydney, NSW 2006, Australia
Abhay K. Singh: School of Accounting, Finance and Economics, Edith Cowan University, Perth, WA 6027, Australia

Risks, 2016, vol. 4, issue 1, 1-14

Abstract: 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 models are evaluated on the basis of error metrics for twenty day out-of-sample forecasts using the mean average percentage errors (MAPE). 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.

Keywords: non linear models; time series; non-parametric; smooth-transition regression models; neural networks; GMDH shell (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (5)

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Related works:
Working Paper: Nonlinear time series and neural-network models of exchange rates between the US dollar and major currencies (2015) Downloads
Working Paper: Nonlinear Time Series and Neural-Network Models of Exchange Rates between the US Dollar and Major Currencies (2015) Downloads
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