ABSTRACT
PurposeThis paper empirically investigates the effect of the coronavirus pandemic (COVID-19) on the Indian financial market and firm betas, perhaps the first paper to do so. The results will be helpful for investors tracking betas during future the coronavirus waves.Design/methodology/approachA conditional capital asset pricing model (CAPM) and multivariate generalized autoregressive conditional heteroskedasticity (GARCH) model is used to estimate time-varying daily betas of the 50 largest Indian stocks spread across 16 industries over five years (Nov 2017 to May 2021), including the two waves of COVID-19 in India.FindingsThe results show that the betas increased during the COVID wave-1 (2020) but not during COVID wave-2 (2021). Moreover, the increase is more pronounced for consumer goods, infrastructure, insurance and information technology, unlike energy (oil and gas, power and mining) industries. Further, there are positive abnormal residual returns during the COVID waves. The results will be helpful for investors tracking betas during future COVID-19 waves.Originality/valueThis is perhaps the first paper to study the firm betas in light of the COVID-19 pandemic.
ABSTRACT
Investors' behavior and news or events occurring during the market closure affect open price variation. For these reasons, daytime and overnight returns and volatilities move differently. In light of these effects, it is natural to ask whether systematic risk varies between trading and non-trading periods. We answer this question by evaluating the US stocks’ beta from 1992 to 2020. Computing daily, daytime, and overnight betas using a proper market index for each trading period, we show how market risk varies over time and is influenced by market shocks. We observe a high overnight risk concentrated within small and large stocks. We find that daytime systematic risk is generally higher, especially between 2001 and 2019. Furthermore, we show how the outbreak of the Covid-19 pandemic led to an increase in overnight risk implying that US stocks became more sensitive to the market closure.