A new approach to forecasting exchange rates

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...

Forecasting Exchange Rates: a Robust Regression Approach

The least squares estimation method as well as other ordinary estimation method for regression models can be severely affected by a small number of outliers, thus providing poor out-ofsample forecasts. This paper suggests a robust regression approach, based on the S-estimation method, to construct forecasting models that are less sensitive to data contamination by outliers. A robust linear auto...

Forecasting exchange rates: An optimal approach

This paper looks at forecasting daily exchange rates for the United Kingdom, European Union, and China. Here, the authors evaluate the forecasting performance of neural networks (NN), vector singular spectrum analysis (VSSA), and recurrent singular spectrum analysis (RSSA) for forecasting exchange rates in these countries. The authors find statistically significant evidence based on the RMSE, t...

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...