Forecasting realized volatility with wavelet decomposition

Abstract

Forecasting Realized Volatility (RV) is of paramount importance for both academics and practitioners. During recent decades, academic literature has made substantial progress both in terms of methods and predictors under consideration albeit with scarce reference to technical indicators. This paper examines the out-of-sample forecasting performance of technical indicators for S&P500 RV relative to macroeconomic predictors. Our main contribution is to demonstrate that these sets of predictors impact volatility at different frequencies and thus are complementary. Specifically, technical indicators perform especially strongly for forecasting the short frequency component which complements macroeconomic variables which perform strongly at longer frequencies. We demonstrate that amalgamation forecasts from these predictors that takes into account the frequency dimension leads to substantial improvements in forecast accuracy.