Modeling and Forecasting Exchange Rates

Nonlinearity In Exchange Rates and Forecasting

Forecasting Disconnected Exchange Rates

Catalyzed by the work of Meese and Rogoff (1983), a large literature has documented the inability of empirical models to accurately forecast exchange rates out-of-sample. This paper extends the literature by introducing an empirical strategy that endogenously builds forecast models from a broad set of conventional exchange rate signals. The method is extremely flexible, allowing for potentially...

nonlinearity in exchange rates and forecasting

Forecasting foreign exchange rates with

(2013) Forecasting foreign exchange rates with adaptive neural networks using radial basis functions and particle swarm optimization. The content must not be changed in any way or reproduced in any format or medium without the formal permission of the copyright holder(s) Forecasting foreign exchange rates with adaptive neural networks using radial-basis functions and particle swarm optimization...

Forecasting Exchange Rates: A Neuro-Fuzzy Approach

This paper presents an adaptive neuro-fuzzy inference system (ANFIS) for USD/JPY exchange rates forecasting. Previous work often used time series techniques and neural networks (NN). ANFIS can be used to better explain solutions to users than completely black-box models, such as NN. The proposed neurofuzzy rule based system applies some technical and fundamental indexes as input variables. In o...