nonlinear time series and neural-network models of exchange rates between the us dollar and major currencies

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ID: 230937
2016
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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.
Reference Key
allen2016risksnonlinear Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;David E. Allen;Michael McAleer;Shelton Peiris;Abhay K. Singh
Journal world neurosurgery
Year 2016
DOI
10.3390/risks4010007
URL
http://www.mdpi.com/2227-9091/4/1/7
Keywords
neural networks time series non-parametric gmdh shellinsurance

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