Abstract:
The accurate forecasting of time series is challenging and for exchange rate is more problematic as well. Because it is difficult to predict as they continuously fluctuate during trading hours. Exchange rate forecasting is a vital financial problem in the recent era. It is extensively acknowledged that exchange rate stability implies macroeconomic stability. In this study, a hybrid model is proposed to forecast exchange rates. Bayesian regularized neural network (BRNN) model is assembled with Autoregressive integrated moving average model (ARIMA) and develop hybrid BRNN-ARIMA model. Furthermore, the comparison of the proposed hybrid model has been done with standalone BRNN, standalone ARIMA, and random walk model. Quarterly exchange rate data from 1970Q1 to 2021Q2 of six countries comprising developed (UK, Canada, and Singapore) and developing (Pakistan, India, and Malaysia) are forecast. To evaluate the performance of these models RMSE, MAE and MAPE are applied. The results indicate that the proposed hybrid BRNN-ARIMA model outperforms the other studied model in forecasting exchange rates.
Page(s):
62-71
DOI:
DOI not available
Published:
Journal: Sukkur IBA Journal of Computing and Mathematical Sciences, Volume: 6, Issue: 1, Year: 2022
Keywords:
ARIMA
,
Bayesian regularized neural network
,
random walk
,
Exchange rate