The Predictive Power of Implied Volatility and GARCH Forecasted Volatility During the COVID-19 Pandemic: Evidence from Chinas Stock Market

Abstract

In this study, we explore how the performance of several popular historical and forward-looking forecasting measures for equity index volatility is affected by the COVID-19 related uncertainty. Our findings present convincing evidence for the advantages of implied volatility in predicting future volatility in the context of the COVID-19 pandemic. Our results also reveal that GARCH forecasted volatility contains unique information about market risk, but the information efficiency is sensitive to economic uncertainty. Therefore, the empirical evidence from Chinas stock market is supportive of a popular theoretical view that GARCH and implied volatility capture different aspects of market uncertainty and an appropriate combination of both measures may perform best in terms of information efficiency. This article contributes to the stream of postCOVID19 literature on its impact on financial markets and provides useful implications for financial practitioners.