Directionality and volatility in high-frequency time series

Abstract

We provide empirical evidence of directionality in high-frequency multivariate time series of the five largest U.S. banks between 1999 and 2017. The directionality is more apparent during crisis periods than during noncrisis periods, and it has only a low association with volatility. We use directionality and volatility as a regime-switching criterion between two-regime threshold vector autoregressive (TVAR) models for forecasting share prices. We compare the forecasting performances using mean relative error squared, and a weighted average of the forecasting error, with weights based on the estimated conditional variance, for individual model components and as a group. We have demonstrated that moving directionality can provide early warning of increased volatility and crisis periods, and has potential for improving one-step ahead forecasts using TVAR(1) models.