Uncertainty indices and stock market volatility predictability during the global pandemic: evidence from G7 countries

Abstract

ABSTRACT This article attempts to examine the predictability of a significant number of uncertainty indices for the G7 stock market volatility based on a Heterogeneous AutoRegressive Realized Volatility (HARRV) model and a combination forecast framework during the global pandemic COVID-19. We include in our analysis the Infectious Disease Equity Market Volatility (IDEMV), the VIX, the Economic Policy Uncertainty (EPU), the Equity Market Volatility (EMV), the Geopolitical risk (GPR) as well as the crude oil futures’ realized volatility. Out-of-sample evidence shows that models incorporating all uncertainty indices improve forecasting performance for most stock markets’ volatility during a long out-of-sample period and also during the coronavirus period. The results are robust using an alternative volatility model, an alternative realized measure and a recursive window analysis. The predictability of the uncertainty indices is also evaluated through risk management and portfolio loss functions and results suggest that the LASSO combination and a HARRV model including all indices are the most profitable for all stock markets during the global pandemic.