In this study, we empirically investigate the impact of the COVID-19 pandemic on China's stock price volatility during and after its initial outbreak, using time-series daily data covering the period from July to October, 2020 and 2021, respectively.
In the estimation, the ARDL bounds test approach was employed to examine the existence of co-integration and the relationship of long-run and short-run between the new infection rates and stock price volatility, as stable and unstable variables are mixed. The inner-day and inter-day volatility, based on the Shanghai (securities) composite index, are estimated in separate empirical models. In addition, the Inter-bank overnight lending rate (IBOLR) is controlled in order to consider the effect of liquidity and investment cost.
We find that in the initial year (2020) of the epidemic, the new infection rate is negatively correlated to stock prices in the short-term, whereas no significant evidence existed in the long-term, regardless of model specifications. However, after the epidemic's outbreak (2021), the result depicts that new infections increased stock prices in the long-term, and depressed its inner-day volatility in the short-term, which is inconsistent with most investigations. This phenomenon may be due to the fact that investors were more concerned about the withdrawal of monetary easing and fiscal stimulus, which were introduced to fight against the epidemic's impact on economy, than the epidemic itself. This study complements the limitations of most existing studies, which just focus on the period of the epidemic's outbreak, and provides insight into macroeconomic policy making in the era of the post COVID-19 epidemic such as the structural and ordered exit of the stimulating policies, intervention in IBOLR and balance social and economic sustainability.