The Impact of COVID-19 on Financial Volatility: A Pre & Post Analysis Using the EGARCH Model

Abstract

Purpose: This study aimed to critically evaluate the effects of the COVID-19 pandemic on financial market volatility in India, with a special focus on the changes in volatility behaviour of the Nifty 50 index during the pre-COVID and post-COVID periods.
Methodology: To accomplish this goal, we used the daily closing values of the Nifty 50 index as provided by the National Stock Exchange of India between 06 February 2020 and 11 May 2020. The sample was split into a pre-COVID segment (06 February 2020 -20 March 2020) and a post-COVID segment (24 March 2020 -11 May 2020), with 23 March 2020 as the date of the pivotal event. An econometric toolkit was used, such as descriptive statistics, Chow breakpoint test, t-tests, Augmented Dickey-Fuller (ADF) test, and Exponential Generalised Autoregressive Conditional Heteroskedasticity (EGARCH) model, to question volatility dynamics, heteroskedasticity, and clustering behaviour.
Key Results: The Chow breakpoint test revealed beyond any doubt that there was a substantive structural discontinuity in the Nifty index on the epidemiological event date. In line with this, the t-test showed a statistically significant reduction in the index in the post-COVID period. The ADF test showed that the series of returns was stationary. The EGARCH estimates indicated that volatility clustering and antecedent shock dependence were strong in the pre-COVID period; in the post-COVID period, historical returns and shock dependence diminished, indicating that the market was highly sensitive to new information. Together, the volatility in the market increased significantly after the outbreak.
Conclusion: The empirical findings demonstrate that the COVID 19 pandemic sharply reorganised volatility patterns within the Indian equity market. Although the pre-pandemic environment was dominated by volatility clustering, the post-pandemic environment changed dramatically, with a reduced reliance on historic volatility and increased sensitivity to current shocks. Under crisis conditions, the EGARCH framework is useful for capturing asymmetric volatility behaviour.
Future Research Directions: Further questions can be posed to extend this study to the post-pandemic recovery phase to evaluate the continuity of volatility effects. Further studies would also benefit from questioning the implications of pandemic-induced volatility on asset prices, portfolio diversification, and risk management strategies in new financial markets.