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.
Original language | English |
---|---|
Article number | 7 |
Number of pages | 14 |
Journal | Risks |
Volume | 4 |
Issue number | 1 |
DOIs | |
Publication status | Published - 16 Mar 2016 |
Externally published | Yes |
Bibliographical note
Copyright 2016 by the authors; licensee MDPI, Basel, Switzerland. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.Keywords
- non linear models
- time series
- non-parametric
- smooth-transition regression models
- neural networks
- GMDH shell