Modeling and Forecasting High Frequency Currency Exchange Rates. A Comparative Study of ARIMA, ANN, and Hybrid ARIMA-ANN Models

Aims/ Objectives: The study develops comparative results on the modeling and prediction performance of the autoregressive integrated moving average (ARIMA), artificial neural network (ANN) and the hybrid ARIMA-ANN time series models for high frequency data. Methodology: The study made use of ARIMA, ANN, and hybrid ARIMA-ANN models to forecasts the East Africa Community countries' daily currency exchange rates data which were obtained from the Central Bank of Kenya website and covered the period from January 2017 to December 2023. Stationarity of the time series data was established using the ADF test. The Ljung Box test and ACF plots were used to establish and compare the goodness-of-fit of the resultant models while the Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error(RMSE) values were used to compare the prediction performance. Results: The study established that the hybrid ARIMA-ANN methodolgy provided better-fitting models for the currency exchange rates data compared to ARIMA and ANN modeling strategies since all the Lyung Box test statistics had p values greater than 5%. Comparatively, the hybrid methodology registered lower MAPE and RMSE values hence had better prediction accuracy compared to ARIMA and ANN methods. Conclusion: The Hybid methodology improves the modeling and forecasting accuracy over the ARIMA and ANN models for high frequency time series data due to its ability to captures both the linear and nonlinear patterns in the time series data.