Forecasting Exchange Rate Realized Volatility: An Amalgamation Approach

Abstract

The importance of Realized Volatility (RV) forecasting in exchange rates has both practical and academic merit. Our aim is to provide a comprehensive analysis of the forecasting ability of financial and macroeconomics variables for future exchange rate realized volatility. We employ seven widely traded currencies against the USD and examine linear models and a variety of machine learning, dimensionality reduction and forecast combination approaches, along with creating a grand forecast (amalgamation approach) from these approaches. Our findings highlight the predictive power of the amalgamation approach, as well as the positive contribution of macroeconomic and financial variables in the forecasting experiment. Furthermore, we generate forecasts on the separate frequencies  of RV using wavelet analysis, in order to extract frequency-related information and examine timing effects in the performance of the methods.

Keywords: Exchange rates, Volatility forecasting, Forecast Combination, machine learning, dimensionality reduction, wavelet decomposition