Analyzing The Covid-19 Pandemic of Volatility Spillover Influence the Collaboration of Foreign and Indian Stock Markets

Abstract

One of the most crucial variables in investment selections is volatility. Unexpected information causes an investor to trade unusually in the market, which influences market volatility. Furthermore, various market sectors are affected differently by this type of trading behaviour. This research investigates the impact of COVID-19 on stock market volatility in India using a generalised autoregressive conditional model. The research was conducted using daily closing prices of stock indices include Nifty 50 and Nifty 500, from September 8, 2019, to July 9, 2021. In this article, the TGARCH model (1,1) was utilized to evaluate the volatility of NSE listed shares. The stock market's volatility has been calculated using the NSE's closing price. To reduce the skewness in the stock price data distribution, the natural logarithm of each price data is employed in the estimations. During the pre-COVID and COVID periods, the conditional volatility of the daily return series showed signs of volatility variations. Furthermore, the study aimed to compare stock price returns in pre-COVID19 and post-COVID19 scenarios to global indexes such as the NASDAQ, Nikkei 225, and FTSE. The stock market in India suffered volatility throughout the epidemic, according to the findings. Consequently, the study recommends NSE stock exchange bond indices to explore the volatility spillover influence between foreign exchange and the stock market in India. In this work, the positive definite covariance matrix is given, therefore a multivariate GARCH with BEKK model is used to estimate the covariance correlation and identify the consequences that stock market downturns can create. SPSS and Eviews software are used to analyze the data. The Augmented Dickey-Fuller (ADF) and KPSS unit root tests have been used to determine whether a time series is stationary or nonstationary. Whereas it corrects for heteroscedasticity and autocorrelation consistency in ADF test statistics, the study employed the KPSS unit root test to estimate the right result. In addition, to investigate the impact of COVID19 on stock market volatility in terms of negative and positive shocks in financial decisions, the TGARCH model captures asymmetry. The finding that the variable has a negative and statistically significant coefficient suggests that the COVID-19 outbreak lowered stock market volatility in India. In terms of historical errors, the coefficients represent the persistence of volatility for each nation. NIFTY and NASDAQ have the largest and longest-term spillover effect. According to the findings, India is the least sensitive to external shocks.