key: cord-0769357-ewl2i9lt
authors: Bora, Debakshi; Basistha, Daisy
title: The outbreak of COVID‐19 pandemic and its impact on stock market volatility: Evidence from a worst‐affected economy
date: 2021-02-11
journal: J Public Aff
DOI: 10.1002/pa.2623
sha: 1ea956a1169ba3af395c33c12857c953a39801b0
doc_id: 769357
cord_uid: ewl2i9lt

This paper empirically investigates the impact of COVID‐19 on the volatility of stock prices in India with the help of a generalized autoregressive conditional heteroscedasticity model. Daily closing prices of stock indices, Nifty and Sensex from September 3, 2019 to July 10, 2020 has been used for the analysis. Further, the study has been attempted to make a comparison of stock price return in pre‐COVID‐19 and during COVID‐19 situation. Findings reveal that the stock market in India has experienced volatility during the pandemic period. While comparing the result during COVID period with that of the pre‐COVID, we found that the return on the indices is higher in the pre‐COVID‐19 period than during COVID‐19.

The rapid spread of the unprecedented COVID-19 pandemic has put the world in jeopardy and changed the global outlook unexpectedly.

Initially, the SARS-CoV-2 virus, which caused the COVID-19 outbreak triggered in Wuhan city, Hubei province of China in December 2019, and with time it spread all over the globe. This pandemic is not only a global health emergency but also a significant global economic downturn too. As many countries adopt strict quarantine policies to fight the unseen pandemic, their economic activities are suddenly shut down. Transports being limited and even restricted among countries have slowed down global economic activities. Most importantly, consumers and firms have prevented their usual consumption patterns due to the creation of panic among them and created market abnormality. Uncertainty and risk created due to this pandemic, causing significant economic impact all over the globe affecting both advanced and emerging economies such as the United States, Spain, Italy, Brazil, and India. In this context, the financial market has responded with dramatic movement and adversely affected. Economic turmoil associated with COVID-19 has affected the financial market severely which includes both stock and bond markets. Due to this pandemic, there is a large fall in the price of oil and a large increase in the price of gold. Firzli (2020) , refers to this pandemic as "the greater financial crisis." In many countries, businesses are highly indebted, weak companies are further destabilized, and corporate debt stands at a very high level.

The global financial market risk has increased substantially in response to the pandemic (Zhang et al., 2020) . Investors are suffering sufficient losses due to fear and uncertainty. For example, due to the impact of this pandemic, the global stock market has struck out about US$6 trillion in 1 week from 24 to 28 February (Ozili & Arun, 2020) . The market value of standard & poor (S&P) 500 indexes declined to 30% since the COVID-19 outbreak. According to Azimili (2020) increased uncertainty affects the required rate of return and thus the current market value of stocks.

Although there is limited current literature related to the impact of COVID-19 on the financial market, the existing empirical studies have provided an exciting result. Baret et al. (2020) , in their research on financial markets and banks, have found that there is a fall in the share of oil, equity, and bonds throughout the world as a result of the COVID-19 pandemic. Social distancing measures adversely affected the productivity of the companies and brought about a decrease in revenue, higher operating cost, and also cash flow challenges to the companies. In Europe, the Financial Times Stock Exchange 100 index witnessed a sharp 1-day fall since 1987 (BBC News, 2020). Igwe (2020) is of the view that the shock from this pandemic can increase the volatility that can negatively affect the economic and financial system of every country. Most of the developed and developing countries' financial markets are adversely affected by this unexpected pandemic. The leading economy of the world, the US stock market hit the circuit breaker mechanism four times in 10 days in March 2020 (Zhang et al., 2020) . The stock market of Europe and Asia has also jumped. United Kingdom's leading index FTSE has fallen more than 10% on March 12, 2020 (Zhang et al., 2020) . Vishnoi and Mookerjee (2020) observed that the stock market in Japan had dropped more than 20% in December 2019. The stock market of Spain, Hong Kong, and China also declined to 25.1, 14.75, and 12.1% in their price from March 8, 2020 to March 18, 2020 (Shehzad et al., 2020) . In his study, also found a harmful impact of the COVID-19 on stock returns of the S&P 500 and an inconsequential impact on the Nasdaq composite index. Georgieva (2020) pointed out that the COVID-19 pandemic brought the entire globe near to financial crises more hazardous than Global Crises

Gradually the worst effect of the pandemic spread to the emerging economy too. If we consider the financial market of the emerging economy a gloomy picture caught our eyes as this economy is worsthit by the collapse of oil prices. The outbreak of the COVID-19 pandemic makes this picture more critical. The top leading emerging economies such as Brazil, Russia, and Mexico gradually moved toward hard mobility restrictions that will bring down the emerging economies to a recession of 1% in 2020 (Herfero, ). In South Korea, the Coronavirus disease caused KOSPI to drop below 1,600 in their history after 10 years (So, 2020) . In China, higher uncertainty due to COVID-19 results in greater volatility of stock return (Leduc & Liu, 2020) . The government of India announced Janata Curfew on March 22, 2020 and lockdown policy to maintain social distancing practice to slow down the outbreaks from March 24, 2020. As the government announced such a lockdown policy, various economic activities have been stopped suddenly. The financial market of India is witnessed sharp volatility as a result of the disruption of the global market (Raja Ram, 2020). As a result of the fall out in the global financial market, the Indian stock market also witnesses sharp volatility. It has also borne the brunt of the COVID-19 pandemic.

There are two major stock indices in India-Bombay Stock Exchange (BSE), Sensex, and National Stock Exchange (NSE), Nifty. If we look at the Bombay Stock Exchange there is a drop in the Sensex index to 13.2% on March 23, 2020. It was the highest single they fall after the news of the Harshad Mehta Scam, April 28, 1991 (Mandal, 2020) . Similarly, Nifty has also declined to almost 29% during this period. Some economists have considered the impact of COVID-19 on the Indian stock market as a "black swan event," that is, the occurrence of a highly unanticipated event with an extremely bad impact. Due to the lockdown policy adopted by the government, the factories have reduced the size of their labor force as well as production level which disrupted the supply chain. Again, because of the uncertainty prevailing among mankind, people also reduce their consumption habits leading to demand-side shock. Studies have also found that the entire previous pandemic had affected only the demand chain. But this COVID-19 pandemic has affected both the demand chain and supply chain.

Despite the several literatures on the impact of COVID-19 on the stock market of the entire economy, there is limited study on it especially in the case of an emerging economy. To shed light on this aspect, this paper attempts to investigate the impact of COVID-19 on the two important stock market of India. Glosten-Jagannathan--

(GJR) generalized autoregressive conditional heteroscedasticity (GJR GARCH) model is used to make the study more significant in terms of volatility in stock index prices due to the outbreak of the pandemic and lockdown policy adopted by the Indian Government. Major findings of the study reveal the volatile nature of BSE Sensex and NSE Nifty, the two prominent stock market of India.

This paper is organized as follows. Section 1 starts with an introduction, Section 2 represents a literature review, Section 3 describes the sources of data and methodology, Section 4 shows results and discussion, and Section 5 ends with the conclusion.

The impact of COVID-19 on the financial market as well as the stock market has been subject to many empirical studies both in advanced and emerging economies. Existing literature found diverse results in these regards. Ozili and Arun (2020) for the economy as the country is highly dependent on oil revenue.

There is a huge gap between the depreciated exchange rate, that is, 20% and the fall in oil prices, that is, 70-80%. According to Herrero (2020), the third wave of the COVID-19 pandemic has hit the emerging economy worst resulting decrease in business activities. This unprecedented shock increases the risk-averse nature which increases the financial cost. Latin America is affected worst because of its much dependency on external financing. Due to the restriction on transport, export has declined. Restriction in the international movement has hampered the tourism sector leading to a fall in revenue. Hyun-Jung (2020) has made a study on the stock market of South Korea, another leading country of the emerging economies. In his analysis, it was found that the economy has shown a roller-coaster ride. The monthly export shows a downtrend in January, improved in February, then again dipped down in March and June. The country's export volume has come down to 11.2% point in comparison to the previous

year. Topcu and Gulal (2020) events the author has considered COVID-19 also a "black swan" event.

He has further analyzed the history of the crash and recovery of the Indian stock market and concluded that the economist cannot predict the recovery of the economy until a stable public health system. Ravi (2020) has compared the pre-COVID-19 and during COVID-19 situation of the In this paper, the closing price of BSE and NSE has been considered for analyzing the volatility of the stock market. In the estimations, we take the natural logarithm of each price data to reduce the observed skewness in the stock price data distribution.

The return of both BSE and NSE has been also calculated to investigate the scenario of change in stock price return during pre-COVID and the COVID period. To calculate the return, the following formula has been used (Osagie et al., 2020) :

Here, R t , P t , and P t − 1 represent the day-wise return, the closing price of the stock at time t, and the previous day's closing price at time t − 1, respectively, while ln symbolizes the natural log.

To check whether a time series is stationary or nonstationary, 

Here, Δ represents first difference operator, p symbolized lag, α 0 represents constant, γ 1 and β i are parameters, and ℇ t denotes a stochastic error term. If γ = 0, then the series is said that it is a unit root and nonstationary.

ADF test add lagged difference term of the regression to take care of possible serial correlation in the error term. On the other hand, PP use nonparametric serial correlation method to take care of serial correlation in the error term without adding lagged difference term (Gujrati, 2016) . For this reason, PP test can be considered more advantageous than ADF test.

The PP test is based on the estimate of the following regression:

Here, α symbolizes constant, ρ represents parameter, and ℇ t denotes residual.

To analyze the effect of COVID-19 on the stock market volatility GJR GARCH model is used. The GJR GARCH model developed by Glosten et al. (1993) and Zakoian (1994) is used to capture asymmetric in terms of negative and positive shocks in the financial decision.

One of the limitations of the GARCH model is that this model imposes a symmetric volatility response to positive and negative shocks (Sakthivel et al., 2014) . This is due to the reason that conditional variance in Equation (4) is the magnitude of the lagged residuals and therefore does not account for their sign.

This asymmetric response of conditional volatility to information can be captured by including, along with the standard GARCH variables, squared values of ε t − 1 when ε t − 1 negative (Glosten et al., 1993) . The GJR GARCH model is estimated as follows:

where I t − 1 = 1 if ℇ t − 1 < 0; =0 otherwise.

γ is known as asymmetry or leverage term. If γ > 0 represents asymmetry while γ = 0 represents symmetry. The condition for nonnegativity would now be α 0 ≥ 0, α 1 ≥ 0, β 1 ≥ 0, and α 1 + γ 1 ≥ 0. In the model, the good news (ε t − 1 > 0) and bad news (ε t − 1 < 0) have contrasting impacts on the conditional variance, good news has an effect of β 1 , while bad news has an effect of α 1 + γ 1 . If γ 1 > 0, negative shocks tend to have more volatility and is known as the leverage effect of the ith order. If γ 1 = 0, the news effect is symmetric.

A dummy variable is introduced in the conditional mean and variance equation to investigate the impact of the COVID-19 outbreak on the volatility of NSE and BSE. The model modified as per the GJR GARCH approach is specified as:

The dummy variable D 1 assumes the value 0 for the pre-COVID-19 era and 1 for the during COVID-19 era. A negative and statistically significant coefficient for the dummy variable implies that the COVID-19 pandemic caused a reduction in the volatility of the Indian stock market. A positive and statistically significant coefficient for the dummy variable implies that the COVID-19 crisis has caused an increase in the volatility of the Indian stock market.

This paper uses the daily price and return of two stock indices of India, BSE, and NSE. First and foremost, we calculate the descriptive statistics of the price and return of the BSE and NSE series. In Table 1 , the mean return which is a major indicator of profit shows a negative value, indicating a loss in stock. Negatively skewed return with higher kurtosis value indicates chances of high losses in both the stock markets. Likewise, the return of pre-COVID-19 and during COVID-19 is presented in Table 2 . As India reported the first case of COVID-19 on January 30, 2020, before this period is considered to be as the pre-COVID-19 era and the period after January 30, 2020 is considered as the during COVID-19 period for the study. In Table 2 April 2020, it again shows a positive trend. This is because relaxation has been adopted in the case of a lockdown policy from April by the government.

Figures 3 and 4 present the log return of BSE and NSE from the period September 3, 2019 to July 10, 2020 and evidence of volatility is shown with the help of these two diagrams. The result depicts that BSE is more volatile than NSE. As we all know that BSE is the largest stock exchange in India, a huge number of investors from different parts of the world make investment in this market. So in terms of volatility BSE is more sensitive in comparison to NSE.

To check the stationarity of two indices, BSE and NSE, we perform ADF and PP stationarity tests. The result presented in Table 3 revealed that most of the log indices are nonstationary in level form, hence the null hypothesis is accepted. Although, log indices have been found stationary in the first difference in both ADF and PP tests. Consequently, the indices are found stationary in first deference. Therefore, the unit root tests justify the existence of stationarity at the first difference. Table 4 presents the estimated results on the GJR GARCH (1,1) model with BSE Sensex and from this table, it has been observed that the coefficient of asymmetric (λ 1 ) and GARCH (β 1 ) are significant. The coefficient of ARCH (α 1 ) is found negative but significant; this particular finding indicates the existence of the ARCH effect in the BSE Sensex series. Further, the coefficient of GARCH was appeared positive and significant, which implies that volatility clustering was present in the BSE index. The positive and significant asymmetric effect also indicate the presence of asymmetric effect and this implies that negative shocks news tend to increase volatility more than positive shocks. To capture volatility, a dummy variable (D 1 ) has been added in both mean and variance equation; D 1 takes the value of 0 and 1 for the pre and during the COVID-19 era, respectively. The result exhibits that the coefficient of the dummy variable for BSE Sensex in the mean equation is negative but not significant. Conversely, in the variance equation, it is positive and significant. This inferred that the spot market volatility in the BSE stock market has increased during the COVID-19 period. must have to shift their investment from a bleak prospect to the bright one to balance their work and avoid risk. In this aspect, the pharma sector is looking attractive at this time. To maintain inclusive and sustainable growth domestic policies will need to be designed. Financial assistance must have to be provided by the supreme authority to the destroyed required sectors.

The data set is available on request.

ORCID Debakshi Bora https://orcid.org/0000-0001-8585-5419

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Her research interest includes rural-urban migration issues and growth convergence in the North-Eastern region of India. She has published different papers in UGC care listed and scopus indexed journals. How to cite this article: Bora D, Basistha D. The outbreak of COVID-19 pandemic and its impact on stock market volatility: Evidence from a worst-affected economy

Debakshi Bora is currently working as an Assistant Professor in the Department of Economics, Assam Women's University, Jorhat, Assam. She is also pursuing PhD in Economics from Dibrugarh University, Assam. Her area of interest includes study of livelihood diversification and human development among various tribes of Assam as well as North East. Bora has published various research paper in peer-reviewed and scopus indexed journals.Daisy Basistha is currently working as an assistant professor in the department of Economics, Bihpuria College, Bihpuria. She is