Year-end Sale % OFF 
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.
Highlights
The Global Financial Crisis (GFC) had a major and sustained impact on the world’s financial markets
This paper examines whether the exchange rate behaviour of four major currencies; namely the Euro, British pound, Chinese Yuan, and Japanese yen, in the context of their paired relationships with the US dollar, is better captured using a variety of nonlinear autoregressive models or by a machine learning approach
Risks 2016, 4, 7 autoregressive model (NNET), and additive nonlinear autoregressive model (AAR), and further models based on the application of various regression specifications of neural network models
Summary
The Global Financial Crisis (GFC) had a major and sustained impact on the world’s financial markets. Franses and van Dijk (2000) [1] mention that nonlinear time series models have become fashionable tools to describe and forecast economic time series They have been applied to macro-economic and financial variables such as unemployment, industrial production, and exchange rates. Economic and financial systems are known to frequently exhibit both structural and behavioral changes, it follows that it may be neccessary to adopt different time series models to explain the empirical data at different points in time. This is apparent in modelling exchange rate behaviour
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
