A SYNERGISTIC FORECASTING MODEL FOR HIGH-FREQUENCY FOREIGN EXCHANGE DATA

Abstract

In this study, we develop a synergistic forecasting model using the information fusion approach. By using high frequency (one-minute) foreign exchange (FX) data, the model fuses two standalone models, namely the technical analysis structural model and the intra-market model. Subsequently, the outputs are fed into a unique modified extended Kalman filter whose functional parameters are estimated dynamically by using an artificial neural network. The synergistic model is tested on four currency pairs that dominate the FX market. In terms of forecasting performance, both root mean squared error and correct directional change performance results show that the synergistic model is statistically outperform and superior to each of the both standalone models as well as to the benchmark random walk model in the literature.