Demystifying the Impact of COVID-19 on Structural Volatility of Indian Sectoral Market: A Quantile Regression Analysis

Abstract

The present study explores the impact of COVID-19 on the volatility structure of the sectoral market in India. ARMA(p,q)- GJR-GARCH(1, 1)-std model is used to determine the daily conditional volatility for 13 selected sectors over the period starting from January 2020 to December 2021. The quantile regression model is employed to examine the changes in the structure of volatility in each sector over the pandemic duration. The results of the study show that the volatility of Metal, Oil–Gas and PSU are more sensitive to market volatility, whereas the volume of new COVID-19 cases exceeds the threshold limit, and no extreme spillover is observed from the market volatility. In addition to this, Bankex, Metal, Oil–Gas, Private Banks and Power sector volatility are more responsive to news sentiments during the period of increase in new COVID-19 cases. Furthermore, the results also reveal that news sentiments help to control the significant fluctuation in the sectoral market.

COVID-19 Pandemic and Indices Volatility: Evidence from GARCH Models

“…The results reveal that the estimated volatility of all the indices instantaneously increased during the pandemic phase, followed by a steady decline (Lakshmi 2013;Priya and Sharma 2023). Volatility of Metal, Oil, and Gas was found to be more susceptible to market volatility (Mishra et al 2023;Verma and Rathore 2023). Following COVID-19, fluctuations by different indices substantially impacted India (Rajamohan et al 2020).…”