On the predictability of crude oil market : a hybrid multiscale wavelet approach

Abstract

Past research indicates that forecasting is important in understanding price dynamics across assets. We explore the potentiality of multiscale forecasting in the crude oil market by employing a wavelet multiscale analysis on returns and volatilities of Brent and West Texas Intermediate crude oil indices between January 1, 2001, and May 1, 2015. The analysis is based on a shift-invariant discrete wavelet transform, augmented by an entropy-based methodology for determining the optimal timescale decomposition under different market regimes. The empirical results show that the five-step-ahead wavelet forecast that is based on volatilities outperforms the random walk forecast, relative to the wavelet forecast that is based on returns. Optimal wavelet causality forecasting for returns is suggested across all frequencies (i.e., daily-yearly), whereas for volatilities it is suggested only up to quarterly frequencies. These results may have important implications for market efficiency and predictability of prices on the crude oil markets.