Exchange Rate Forecasting Using Ensemble Modeling for Better Policy Implications

Abstract

Abstract This study aims to contribute in the area of foreign exchange forecasting. Exchange rate plays an essential role for the economic policy of a country. Due to the floating exchange rate regime, and ever-changing economic conditions, analysts have observed significant volatility in the exchange rates. However, exchange rate forecasting has been a challenging task before the analysts over the years. Various stakeholders such as the central bank, government, and investors try to maximize the returns and minimize the risk in their decision-making using exchange rate forecasting. The study aims to propose a novel ensemble technique to forecast daily exchange rates for the three most traded currency pairs (EUR/USD, GBP/USD, and JPY/USD). The ensemble technique combines the linear and non-linear time-series forecasting techniques (mean forecast, ARIMA, and neural network) with their most optimal weights. We have taken the data of more than seven years, and the results indicate that the proposed methodology could be an effective technique to forecast better as compared to the component models separately. The study has crucial economic and academic implications. The results derived from this study would be useful for policymakers, regulators, investors, speculators, and arbitrageurs.