Nonlinear time series and neural-network models of exchange rates between the US dollar and major currencies
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.
|Keywords||non linear models, time series, non-parametric, smooth-transition regression models, neural networks, GMDH shell|
|JEL||Neural Networks and Related Topics (jel C45), Forecasting and Other Model Applications (jel C53), International Finance (jel F3), International Financial Markets (jel G15)|
|Series||Econometric Institute Research Papers|
|Note||For financial support, the first author acknowledges the Australian Research Council, and the second author is most grateful to the Australian Research Council, National Science Council, Taiwan, and the Japan Society for the Promotion of Science.|
Allen, D.E, McAleer, M.J, Peiris, S, & Singh, A.K. (2015). Nonlinear time series and neural-network models of exchange rates between the US dollar and major currencies (No. EI2015-33). Econometric Institute Research Papers. Retrieved from http://hdl.handle.net/1765/79217