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

1
School of Mathematics and Statistics, the University of Sydney, Sydney, NSW 2006, Australia
2
Centre for Applied Financial Studies, School of Business, the University of South Australia, Sydney, SA 5001, Australia
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Department of Quantitative Finance, National Tsing Hua University, Taichung 402, Taiwan
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Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam 3000, The Netherlands
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Tinbergen Institute, Rotterdam 3000, The Netherlands
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Department of Quantitative Economics, Complutense University of Madrid, Madrid 28223, Spain
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School of Accounting, Finance and Economics, Edith Cowan University, Perth, WA 6027, Australia
*
Author to whom correspondence should be addressed.
The analysis in the paper was undertaken with R and GMDH shell.
Academic Editor: Mogens Steffensen
Received: 9 November 2015 / Revised: 26 February 2016 / Accepted: 1 March 2016 / Published: 16 March 2016
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. View Full-Text
Keywords: non linear models; time series; non-parametric; smooth-transition regression models; neural networks; GMDH shell non linear models; time series; non-parametric; smooth-transition regression models; neural networks; GMDH shell
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MDPI and ACS Style

Allen, D.E.; McAleer, M.; Peiris, S.; Singh, A.K. Nonlinear Time Series and Neural-Network Models of Exchange Rates between the US Dollar and Major Currencies. Risks 2016, 4, 7. https://doi.org/10.3390/risks4010007

AMA Style

Allen DE, McAleer M, Peiris S, Singh AK. Nonlinear Time Series and Neural-Network Models of Exchange Rates between the US Dollar and Major Currencies. Risks. 2016; 4(1):7. https://doi.org/10.3390/risks4010007

Chicago/Turabian Style

Allen, David E., Michael McAleer, Shelton Peiris, and Abhay K. Singh. 2016. "Nonlinear Time Series and Neural-Network Models of Exchange Rates between the US Dollar and Major Currencies" Risks 4, no. 1: 7. https://doi.org/10.3390/risks4010007

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