In this paper the exchange rate forecasting performance of neural network models are evaluated against random walk and a range of time series models. There are no guidelines available that can be used to choose the parameters of neural network models and therefore the parameters are chosen...

In this paper, the exchange rate forecasting performance of neural network models are evaluated against the random walk, autoregressive moving average and generalised autoregressive conditional heteroskedasticity models. There are no guidelines available that can be used to choose the parameters...

This paper compares the UK/US exchange rate forecasting performance of linear and nonlinear models based on monetary fundamentals, to a random walk (RW) model. Structural breaks are identified and taken into account. The exchange rate forecasting framework is also used for assessing the relative...

In this paper, a Divisia monetary index measure of money is constructed for the Italian economy and its inflation forecasting potential is compared with that of its traditional simple sum counterpart. The powerful and flexible Artificial Intelligence technique of neural networks is used to allow...

Linear models reach their limitations in applications with nonlinearities in the data. In this paper we provide new empirical evidence on the relative Euro inflation forecasting performance of linear and nonlinear models. The well established and widely used univariate ARIMA and multivariate VAR...

In this paper we compare the performance of three indices in an inflation forecasting experiment. The evidence not only suggests that an evolved neural network is superior to traditionally trained networks in the majority of cases, but also that a risky money index performs at least as well as...