




































INDIAN JOURNAL OF FINANCE AND BANKING 10(1) (2022), 18-30 

18 

 

                   FINANCE AND BANKING 

                                                                  IJFB VOL 10 NO 1 (2022) P-ISSN 2574-6081  E-ISSN 2574-609X 
                                                  

        Available online at https://www.cribfb.com 
                                                                                                                                           Journal homepage: https://www.cribfb.com/journal/index.php/ijfb 

                                                                                                                                                                                                   Published by CRIBFB, USA 

THE SHORT-TERM CORONAVIRUS (COVID-19) PANDEMIC 

EFFECT: AN EMPIRICAL INVESTIGATION OF INDIAN STOCK 

MARKET (BSE)          

 
 Mohd Atif Afzal (a)1   Nasreen Khan (b)   Abdul Saboor Mohammad (c)   Mohd Taqi (d)   

 

(a) Assistant Professor, Centre for Distance and Online Education, Aligarh Muslim University, Aligarh 202002, India; E-mail: atifafzalgd7581@gmail.com 
(b) Assistant Professor, Department of Accounting, Al-Baha University, Kingdom of Saudi Arabia; E-mail: findnasreen@gmail.com 
(c) Department of Commerce, Aligarh Muslim University, Aligarh 202002, India; E-mail: abdul.saboor.mohd@gmail.com  
(d) Assistant Professor, Centre for Distance and Online Education, Aligarh Muslim University, Aligarh 202002, India; E-mail: taqiamu@rediffmail.com 

 

 
A R T I C L E I N F O 

 
 

Article History: 
 

Received: 14th March 2022  

Accepted: 10th May 2022 

Online Publication: 13th May 2022 

 
Keywords: 

 

Indian Stock Market, COVID-19, GARCH 

Standard Vector Autoregression, Impulse 

Response Function  

 
JEL Classification Codes: 

 

      H54, R53 
 

 

  

 
A B S T R A C T 
 
India’s precautionary step toward COVID-19 led to 1.3 billion people enduring lockdown, consequently 

halting the wheels of the Indian economy. Though the crash of the stock market was evident and 

explanatory, it left all stakeholders (including the investors and government) with no choice. Deteriorating 

SENSEX and other indices forced investors to withdraw and lose attraction from the market and looped 

in more serious issues to the Indian stock market. This paper empirically analyses the short-term impact 
of COVID-19 on five selected BSE indices for SENSEX, FMCG, Bank, Corporate-Bond, and Industrial 

using econometric models. Stationarity was checked by Augmented Dickey-Fuller and Philips-Perron test. 

Autocorrelation was assessed and mitigated by Durbin-Watson, Breusch-Godfrey Serial Correlation LM 

test, and Cochrane-Orcutt transformation method. This study attempts to apply Multiple Regression, the 

GARCH model, Standard Vector Autoregression (S-VAR), and Impulse Response Function (IRF) to 

decode the relation. The results indicated that the COVID-19 pandemic is adversely affecting the 

performance of the Indian stock market in the short run. All the indices showed a negative relationship 

antecedent with the effect of COVID-19, except for the SPICBI index. This paper implies investment 
solutions for the investors and policymakers to cope with the unprecedented situation amidst COVID-19. 

 
 

© 2022 by the authors. Licensee CRIBFB, USA. This article is an open access article  distributed under 
the terms and conditions of the Creative Commons Attribution (CC BY) license 
(http://creativecommons.org/licenses/by/4.0/).                           

 

INTRODUCTION 

“Fear of the unknown is a terrible fear” says -Joan D Vinge. In this integrated world, pandemic not only hits mortality 

(death) and morbidity (short period layoff) (McKibbin & Fernando, 2020); but uncertain fear and apparent situation affects 

all the economic and non-economic activities. Outbreak of Coronavirus (COVID-19) likely from China (W.H.O., 12 January 

2020) to almost every part of the continent (Worldometer) is likely to affect economic activities at large due to imposition 

of lockdown in various parts of the world (Mudgill, The Economic Times, 25th March 2020). India's lockdown of 1.3 billion 

people which is reported as the biggest lockdown of any region (Al Jazeera, 14 April 2020), will definitely hit economic 

activities in almost every sector. Stock markets are considered the reflection of a country's economic health. The BSE 

SENSEX (Indian stock exchange) within duration of two weeks, dropped from 42,000 to 25,000 (Business Reporter, 23 

March 2020), indicating the severity of the COVID-19. Indian Stock market had also showed negative relations in previous 

epidemics (Vijayakumar et al., 2013), though it had recovered within a short span of time in cases of Severe Acute 

Respiratory Syndrome (SARS), Zika and Ebola (Mehta, The Economic Times, 16 March 2020). The policy makers are 

eyeing a way out of the situation citing COVID-19 can be lethal to Indian economy (Iyer, 2020). Academicians and 

researchers of any field are obligated to participate in exploring past trends and try to suggest corrective measures and 

actions which is the need of the hour. In this research paper, economic impact of COVID-19 on short run is judged 

considering the Indian stock market index of Bombay Stock Exchange (BSE) and it also recommends investor’s interest to 

be protected, mainly through diversifying their investments. 

                                                      
1Corresponding author: ORCID ID: 0000-0001-8308-4622 
© 2022 by the authors. Hosting by CRIBFB. Peer review under responsibility of CRIBFB, USA.  

https://doi.org/10.46281/ijfb.v10i1.1716 

 
To cite this article: Afzal, M. A., Khan, N., Mohammad, A. S., & Taqi, M. (2022). THE SHORT - TERM CORONAVIRUS (COVID-

19) PANDEMIC EFFECT: AN EMPIRICAL INVESTIGATION OF INDIAN STOCK MARKET (BSE). Indian Journal of Finance 

and Banking , 10(1), 18-30. https://doi.org/10.46281/ijfb.v10i1.1716  

http://creativecommons.org/licenses/by/4.0/)
http://creativecommons.org/licenses/by/4.0/)
https://doi.org/10.46281/ijfb.v10i1.1716
https://orcid.org/0000-0001-8308-4622
https://orcid.org/0000-0002-7769-5846
https://orcid.org/0000-0002-2247-8609
https://orcid.org/0000-0002-6701-7267


Afzal et al., Indian Journal of Finance and Banking 10(1) (2022), 18-30 

 

19 

Stock market indices are based on demand of the market and heuristics (or biases) are guiding principle for decision makers 

while buying or creating demand for particular stocks, among which fear plays important role (Hassan et al., 2013). As the 

spread of COVID-19 is globally expanding geometrically and has seen no decline, ensuring fear has resulted in the 

fluctuations of indices of Indian stock market. Thus it can be classified that Indian economy is approaching towards 

“Economic Shock” (Baldwin & di Mauro, 2020). The spread of COVID-19 has dramatically affected the investor-market, 

directly and indirectly. The direct effect is due to close down and lockdown while indirect one is the due to fear caused. 

This study has tried to evaluate the fear basis of an investor. The exposure of the virus is reflected by the active cases and 

mortality which has been closely monitored by many agencies globally (Worldometer) as well as nationally (MyGov.in). 

The short-term impact has been investigated with the help of econometric analysis, where dependent variables are the five 

selected stock indices of BSE whereas and Active Cases (AC) and Daily Death (DD) due to COVID-19 has been taken as 

independent variables. This paper concludes the mitigating strategy of the Indian investors to safeguard their investment 

interest and also acts as buffer to stock market in the negative growth period. The paper’s findings also imply the policy 

makers to re-focus on corporate bond corpus; which has been regarded as the “white elephant” of secondary market; and 

revive the fund as well as to utilize the disguised opportunity. 

 

Pandemic Effect 

A lot of diseases have been discovered, unfurled and controlled; few of them become epidemic, causing threat to humanity 

due to its rapid spread. Seeing, its exposure, W.H.O. declared “public health emergency / outbreak of pandemic” on 12 

March 2020 (W.H.O.) and consequently India announced COVID-19 as “notified disaster” on 14 March 2020 (The 

Economic Times). The history check reveals four pandemics: 1889, 1918, 1957 & 1968 in the last 130 years (Maital & 

Barzani, 2020); though SARS had not qualified as pandemic. 

Ample studies have been conducted in the post-epidemics period and similarly its effect on economy of a country 

has also been reported. Black Death (considered largest pandemic ever recorded) saw drop in labor supply (25-40%); fall 

in returns (5-8%) and increase in real wages (100%) (Clark, 2007; 2010). Yang et al. (1999) and Carpernter (2011) studied 

economic impact of foot and mouth disease in Taiwan and California respectively. In another study, Armien and Halasa 

(2012) evaluated the overall cost involved in an epidemic. Study related to the impact of AIDS on the economic parameters 

is also analyzed by Bloom and Mahal (1997) and suggested that only long term correlations can give predictive results. 

Another study concerning AIDS, concluded negative relation with Gross Domestic Product (GDP) in the context of Ukraine, 

which was done by Barnett et al. (2000). 

SARS, which has been extensively studied (Gupta, 2005; Chen et al., 2007; Beutels et al., 2009) has noticed 

significant impact on the socio-economic health of affected countries. The Chinese stock market has been analyzed by 

Beutels et al. (2009) and was reported to have negative effect. In the Indian context, very few studies related to epidemics 

can be found such as Vijayakumar et al. (2013); where researcher exposed the relation of Chikungunya epidemic on per 

capita income (monthly) and hence concluded no relationship. 

In a very recent study, anticipating impact of COVID-19 on Chinese economy, Ayittey et al. (2020) found that 

world may lose 280 billion US$ which would wipe approximately 0.5% of world’s GDP (Riley and Horowitz, CNN 

Business, 10 February 2020). In another study, the estimate of COVID-19 impact on world GDP was calculated to drop 1% 

(Luo & Tsang, 2020). Estrada et al. (2020) simulated the impact and concluded astonishing figures of 4 trillion US$ drop 

in Chinese GDP. 

 

Economic Impact of COVID-19 

The first patient of COVID-19 was reported in December in Wuhan (China), after which it got spread to various parts of the 

world (Worldometer). Historically, epidemics have affected this planet from time to time and all the aspects (economic and 

non-economic) are influenced in the course. Human intervention includes the development of vaccine, prevention of its 

communicability and the societal awareness. Aforementioned prevention leads to major governmental obligation, of 

controlling the movement of people somehow, which deliberately restricts the economic activity (Ellis-Peterson, The 

Guardian, 24th March 2020). Similarly, COVID-19, when declared pandemic, became a serious threat to the society, both 

clinically and economically. In India, where majority of the sectors are unorganized (Nagaraj, 2013), this employing 82 % 

of casual workforce of India reported by National Institution for Transforming India (NITI) Aayog (Mohanty, 2019), thus, 

it is difficult to cope up with the situation. The government decisions are typically a tradeoff between functioning of the 

economy and prevention of the epidemic.  

 

Indian Stock Exchange 

India’s leading news headlines read “The virus that has spooked the world’s markets and sparked fears of global recession 

has also played havoc with India’s macro indicators” (Mehta, 23 March 2020). The fall was in sync with the world market. 

The stock exchange of India consists of nine official exchanges, of which BSE (selected for the study) is reputed globally. 

BSE is here considered for the study which is Asia's oldest stock exchange, established in 1875 (BSE, n.d.) and having more 

than 2.2 trillion estimation of market capitalization (10th spot by size). BSE is the largest stock market in terms of number 

of companies with 5749 listed companies (Shukla, 2019 February). When essential and non-essential sectors are plunged to 

lowest level, investment and secondary markets are the first objects that losses their strength. This is evident by the drop of 

BSE SENSEX which got closed at 25,981 points in the last week of March (Business Reporter, 23 March 2020). The study 

has taken account of the movement of stock market indices (here BSE) as dependent variables, discussed in next section. 

Due to the recent nature of the subject, not a lot of studies are found. Studies in scientific and non-scientific fields are absent 

and this paper primarily aims at filling this gap. Secondly, economic trends, considering stock indices are ample in general 



Afzal et al., Indian Journal of Finance and Banking 10(1) (2022), 18-30 

 

20 

phenomenon; but very few studies could be unearthed related to the epidemic, or the disease-caused change, or even 

pandemic. Thirdly, no studies are found related to epidemic in the Indian context, ignoring exploratory news editorials. 

Fourthly, except for two studies: viz Luo and Tsang (2020) and Estrada et al. (2020), where researcher had scientifically 

explored the relation using statistical model. This study attempts to apply different statistical tools and techniques such as 

multiple regression, GARCH model, Standard Vector Autoregression (S-VAR) and Impulse Response Function (IRF) to 

decode the relation. Lastly, this study aims at providing recommendations for the policy makers to encourage the investors 

who have lost their money and hope in the stock market, and to revive the overall condition of the market. 

In line with the literature, the general objective of this study is to investigate the impact of COVID-19 on the Indian 

stock market. This can be further classified infers of SENSEX and other stock indices of Fast Moving Consumer Goods 

(FMCG), Banks, Bond-market and Industrial. 

 

MATERIALS AND METHODS 

The selection of BSE indices are based on its global popularity which are regarded as benchmark indices (cleartax.com). 

There are a few lists of indices traded on the podium of BSE. The research design of this paper is based on secondary data, 

which comprises daily-data of five stock indices of BSE and cases of COVID-19 from 30th January 2020 to 3rd July 2020. 

The selected indices are S&P BSE SENSEX, S&P BSE FMCG Index, S&P BSE BANKEX, S&P BSE India Corporate 

Bond Index and S&P BSE Industrials which are proxy to Indian stock market. The daily data of COVID-19 active cases 

and deaths have been retrieved from the worldometer website. The data of five indices have been extracted from the official 

website of BSE (See Table 1). The data of S&P BSE India Corporate Bond Index has been retrieved from website of Asia 

Index Pvt. Ltd. The return has been calculated as per (Pt - Pt-1) / Pt-1*100. The selection of the indices and their importance 

in the current scenario with references to the sectors are discussed below. 

 

Table 1. Variables, Measurement and Abbreviation 

 
Variables Measurements Abbreviations 

Dependent Variables 

S&P BSE SENSEX Index Daily Closing Price SENSEX 

S&P BSE India Corporate Bond 

Index 

Daily Closing Price SPICBI 

S&P BSE FMCG Index Daily Closing Price FMCG 

S&P BSE BANKEX Index 

S&P BSE Industrials Index 

Daily Closing Price 

Daily Closing Price 

BANK 

IN 

Independent Variables 

Active Cases of COVID-19 Excluding  Recovery and 
Death from the total cases 

AC 

Daily Death (due to COVID-19) Death Per Day DD 

                                           Source: Authors compilation 

 

S&P BSE SENSEX Index 

When the SENSEX (regarded as pulse of domestic stock market), which is the core of BSE stocks, plummeted from 41,952 

(highest achieved) in mid-January 2020 (Business Today, January 2020) to 25,981 in the fourth week of March 2020 

(Business Reporter, 23 March 2020), the COVID-19 effect was evident. It became of utmost importance for the stakeholders 

(government, policy makers and researchers) to draft a way out of the crisis. This papers aims in developing a third way-

out strategy to counter the stock fall and backing the interest of the investors. The selections of indices are correlated with 

international consequences and pandemic scenario. 

 

S&P BSE FMCG Index  

In the case of pandemic, there is a latent pressure within the sectors which are the direct basis of living. Food and beverages 

are the inevitable demands that humans cannot ignore. Moreover, being non-durable, these also affect the supplier’s end. 

FMCG being fourth largest sector of Indian economy amounting to 52.75 Billion US$, 2018 (IBEF, March 2020) will be 

affected by the recent COVID-19, both at demand as well as supply side. Hence, this index will help to identify the short 

term economic impact. 

 

S&P BSE BANKEX Index 

There are three reasons for considering index of banking sector. i) Firstly, Indian economy where majority of the workforce 

is unorganized and sectors are not well-structured, Banking sector is regarded as sufficiently regulated, organized and 

structured. ii) Secondly, being the flow channel of the funds, this sector is greatly responsible for running the economy. 

Banks and credit flow cannot stop even on the last brink, whether it is personal or commercial banking. iii) Lastly, various 

channels of banking include micro- and rural-banking. This reflects the reach of the sector on one hand and the counter 

effect of this sector on the economy as a whole, on the other hand. The index fluctuations are the outcome of the demand 

which in turn is consequent to the market news. Although country's central bank, Reserve Bank of India (RBI) ensured 

customers interest in case of YES BANK crisis, yet fall of the stock eroded 85% of the value (The Economic Times, 7 

March 2020). It is evident how banking sector reacts to the catastrophe. 

 

 

 



Afzal et al., Indian Journal of Finance and Banking 10(1) (2022), 18-30 

 

21 

S&P BSE India Corporate Bond Index and S&P BSE Industrials index 

The fourth construct extracted from BSE is of bond market and this dimension has been explored due to its orthogonal 

relation with other indices. This is due to the fact that bond indices are regarded as low risk, long term and high return index. 

Being a debt security, economics and behavioral rationale of the investor differs, in comparison to other indices. BSE 

corporate bond has been in focus since early 2020 due to its stagnant rise. T. K. Arun (The Economic Times, 14 January 

2020) says that the post Nehruvian-governemt which provided base to the manufacturing portfolios, development of roads 

or infrastructure, is often blamed for ignoring development of private sector. Albeit this paradox, the private sector was 

developed on a large scale for all times. Seeing this, the new Development Financial Institution (DFI) is just an excuse to 

avoid the development of the corporate bond market which has been sluggish and became "headache to Indian borrowers" 

(Patil, The Print, 20th January 2020). The bond market indices have always been in the focus of the economic policy makers. 

Report by RBI has suggested an established and developed bond market can boost economy of any country (Acharya, 2011). 

Achraya further explains that the declining role of DFI and robust corporate bond corpus will help in building economy for 

which India has been re-aiming at its 10.08% of GDP growth (which was achieved during 2006-07) (The Economic Times, 

2018 August).  

This paper aims at indicating abnormality in the movement of the pandemic’s overall effect with other indices and 

suggest insights for three purposes; firstly, countering the crash of the stock market; secondly, favoring the interest of the 

investors and lastly, to re-shuffle the corporate bond market that has been ignored for a long time. The Industrial index (S&P 

BSE Industrials) is chosen owing to its sectorial relevance. Further, experts have opinion that lockdown will have least 

likely impact on the industrial industry. Industrial manufacturing, in a recent study, is said to have the triple hit due to supply 

chain disruptions (Jorda et al., 2020).  

 

RESULTS 

Descriptive Statistics 

The SPICBI has the highest mean return while BANK has the lowest mean return of -0.198 shown in table 2. The mean 

returns of all the indices are negative except in SPICBI and FMCG index. This signifies that investment in Indian stock 

market is not favourable to the investors during the on-going COVID-19 pandemic. However, the mean return value of 

SPICBI and FMCG index reflects that it may be the favourable avenue for the investors where they can generate good 

returns by consciously including Indian corporate bond and FMCG index in their investment portfolio. The standard 

deviation statistic (S.D.) reveals that the BANK is the most volatile and risky index among all the selected indices while the 

S.D. of SPICBI is 0.114, which signifies low volatility and being least risky. 

 

Table 2. Summary Statistics 

 
 SENSEX FMCG AC DD BANK IN SPICBI 

 Mean -0.028  0.0318  7.003  7.534 -0.198 -0.152  0.051 

 Median  0.159  0.001  2.613  4.901  0.076  0.021  0.046 

 Maximum  8.974  8.237  71.654  91.540  10.704  5.386  0.421 

 Minimum -13.152 -10.422 -10.703 -42.187 -16.806 -13.629 -0.282 

 Std. Dev.  2.902  2.372  11.693  26.436  3.789  2.641  0.114 

             Source: Authors compilation 

 

Pre-test Assumptions 

A set of data is said to be stationary when the mean, variance and auto-variance are constant (Brooks, 2008). The non-

stationary data may produce high R2 value and significant relationship, even when the variables are unrelated with each 

other (Salvatore & Reagle, 2002). To check the stationarity of the data, the researchers have applied Augmented Dickey-

Fuller (ADF) test and in order to validate the results of ADF test further, researcher have applied Philips-Perron (PP) test. 

The p-values (shown in table 3) of all the selected variables are less than 0.05. It implies that all the variables are stationary 

at level. 

 

Table 3. Unit Root Testing 

 
Variables At level (ADF) At level (PP) Summary 

SENSEX -11.979 
0.000* 

-11.908 
0.000* 

Stationary at level 

AC -2.067 

0.037** 

-6.339 

0.000* 

Stationary at level 

DD -2.058 
0.0385** 

-13.03 
0.000* 

Stationary at level 

FMCG -12.577 

0.000* 

-12.246 

0.000* 

Stationary at level 

SPICBI -7.416 
0.000* 

-7.375 
0.000* 

Stationary at level 

BANK -10.328 

0.000* 

-10.329 

0.000* 

Stationary at level 

IN -9.696 
0.000* 

-9.793 
0.000* 

Stationary at level 

Note: * and ** denotes p-value at 1% and 5% level of significance, respectively. 

Source: Authors compilation 



Afzal et al., Indian Journal of Finance and Banking 10(1) (2022), 18-30 

 

22 

Multiple Regression 

The econometric models were developed for each index to identify the relationship with COVID-19 (table 4). The F-statistic 

of all the econometric models are less than 0.05 it indicates that all the models are statistically significant. The Durbin-

Watson statistics of all the eight models are within the acceptable range of 1.5 to 2.5 (Garson, 2012; Anselin, 2013) which 

signifies that there is no autocorrelation in the residuals of the econometric models. There is significant effect of AC in all 

econometric models with negative coefficients in SENSEX, FMCG, BANK and IN except for SPICBI which has a positive 

effect (table 5). Considering the effect of DD on the stock indices, results of regressions are showing insignificant 

relationship. 

              If the residuals of the models are serially correlated then it will produce overestimated value of R2, t-value and f-

value; and the value of R2 may not be valid (Gujarati et al., 2012). The Durbin-Watson (D-W) and Breusch-Godfrey Serial 

Correlation LM Test have been used to investigate whether the residuals of the models are auto-correlated or not. 

 

Table 4. Econometric Models 

 
Econometric Models Equations 

Model 1 SENSEXt= β0 + β1ACt+ β2DDt + εt 

Model 2 FMCGt= β0 + β1ACt+ β2DDt + εt 

Model 3 BANKt= β0 + β1ACt+ β2DDt + εt 

Model 4 SPICBIt= β0 + β1ACt+ β2DDt + εt 

Model 5 INt= β0 + β1ACt+ β2DDt + εt 

Where, SENSEXt= S&P BSE SENSEX at time t; ACt= Active Cases at time t; DDt= Daily Death at time t; FMCGt= 

S&P BSE FMCG at time t;SPICBIt= S&P BSE India Corporate Bond Index at time t;INt= S&P BSE 

Industrials;BANKt=S&P BSE BANKEX;β0= Intercept; β1- β2= Coefficients of independent variables; εt= error 

term  

                                                                           Source: Authors compilation 

 

The results depict that all the p-values are > 0.05 implies that models are not facing the issue of serial correlation 

except model 4. A number of statistical tools such as Cochrane-Orcutt (CO) and Prais-Winsten transformation method can 

be applied to counter the issue of auto-correlation. The CO technique is also called as two-step method and it iterates the 

process till the two successive calculations are nearly the same (Sumantri, 2020). The researcher has applied CO estimation 

in order to remove serial correlation from the model 4. The presence of heteroscedasticity in the residuals of the models 

may lead to produce misleading results (Gujarati et al., 2012). The results of the test have been reported in table 6 and p-

values > 0.05 which signifies that residuals are homoscedastic. 

 

Table 5. Regression Results 

 
Variables                                  Dependent Variables 

Independent variables Model 1 

(SENSEX) 

Model 2 

(FMCG) 

Model 3 

(BANK) 

Model 4 

(SPICBI) 

Model 5 

(IN) 

AC Coefficient -0.075 -0.039 -0.101 0.002 -0.086 

Prob. 0.003* 0.060** 0.002* 0.066** 0.000* 

DD Coefficient 0.009 0.010 0.015 -0.000 0.001 

Prob. 0.408 0.262 0.270 0.417 0.884 

R2 0.085 0.039 0.091 0.111 0.144 

Adjusted R2 0.066 0.020 0.073 0.083 0.127 

Durbin-Watson  2.249 2.318 1.987 1.988 1.878 

Prob(F-statistic) 0.0128 0.038 0.008 0.009 0.000 

Note: * and ** denotes p-value at 1% and 10% level of significance, respectively. 
Source: Authors compilation 

 

Table 6. Results of Autocorrelation and Heterocedasticity 

 
Models                                              Autocorrelation Heterocedasticity  

F-statistic Obs*R-squared F-statistic Obs*R-squared 

SENSEX 0.8155 

(0.445) 

1.6874 

(0.430) 

2.6481 

(0.106) 

2.6311 

(0.104) 

FMCG 1.5591 

(0.215) 

3.1774 

(0.204) 

2.7100 

(0.071) 

5.2908 

(0.071) 

BANK 0.1786 

(0.836) 

0.3745 

(0.829) 

0.0012 

(0.972) 

0.0012 

(0.971) 

SPICBI 0.0046 

(0.995) 

0.0098 

(0.995) 

1.2507 

(0.266) 

1.2602 

(0.261) 

IN 1.7010 

(0.187) 

3.4568 

(0.177) 

0.2333 

(0.630) 

0.2375 

(0.626) 

Note: p-values are reported in parentheses. 

Source: Authors compilation 

 

According to Montgomery et al. (2001) the most commonly used technique to check the 

multicollinearity is Variance Inflation Factor (VIF). Therefore, (VIF) method has been applied and each model has been 

run separately to investigate whether the explanatory variables are strongly inter-correlated or not. The result of VIF is 



Afzal et al., Indian Journal of Finance and Banking 10(1) (2022), 18-30 

 

23 

presented in table 7. According to Hair et al. (1995) the threshold limit of VIF should be less than 10. The VIF values of the 

variables are below the acceptable limit i.e. 10, which signify that there is no multicollinearity. 

 

Table 7. Variance Inflation Factor (VIF) 

 
Independent Variables VIF 

AC 1.07438 

DD 1.07438 

          Source: Authors compilation 

 

GARCH Model 

Autoregressive Conditional Heterocedasticity (ARCH) and Generalised ARCH (GARCH) methods are generally used for 

assessing and forecasting the variances. The model formulates variances in the dependent construct as a function of previous 

values of dependent as well as exogenous variables. This technique was developed individually for ARCH by Engle (1982) 

and GARCH by Bollerslev (1986). Widespread acceptance of this methodology is evident by application of ARCH and 

GARCH in many econometric studies such as financial time series (Bollerslev, Chou, and Kroner, 1992; Bollerslev, Engle, 

and Nelson, 1994) and stock market studies (Garg & Bodla, 2011; Shehzad et al.,2020). 

The mean equation adopted from Shehzad et al. (2020) 

 

Rti=β0 + β1ACti+ β2DDti+ εti 

 

Where, Rti is the return of the indices, β0 is the intercept, ACti and DDti are used as a proxy for Covid-19 and εti are the 

residuals term. 

 

The variance equation 

ht = c +α1e2
t-1+β1ht-1 

 

In the above equation the α1 and β1 are referred to as ARCH and GARCH parameters, respectively conditional 

variance is only finite when stationarity condition is fulfilled that is when α1+ β1 should be less than 1. The c term is volatility 

in long term and α1 and β1 can be directly interpreted to be the volatility by fear factor in mind of the investors caused by 

adverse effect of COVID-19.  The short term dynamics of the series is due to size of the α1 and β1 parameters and when the 

value of GARCH coefficient is greater than ARCH coefficient it indicates that the shock effect caused by COVID-19 will 

be persistent in the market for a long period Shehzad et al. (2020). On the contrary if the value of ARCH coefficient is 

greater than GARCH than it indicates spiky fluctuations. To detect autoregressive conditional heteroscedasticity, it requires 

application of ARCH-LM test (Engle, 1982) and the p-value is less than 0.05 (See table 8) that indicates presence of ARCH 

effect in the residuals. 

 

Table 8. ARCH LM TEST 

 
Variables F-Statistics P-Value Obs*R-squared P-Value 

SENSEX 5.565 0.005 10.270 0.005 

AC 2.672 0.026 12.392 0.029 

DD 2.554 0.033 11.915 0.036 

FMCG 6.206 0.014 5.951 0.014 

BANK 2.975 0.035 8.491 0.036 

SPICBI 3.392 0.037 6.529 0.038 

IN 5.713 0.004 10.513 0.005 

Source: Authors compilation 

 

The results of regression models were elaborated by application of GARCH (1,1) model that has been used by many 

researchers in the context of stock market particularly in Indian setting (Garg & Bodla, 2011). Variable of COVID-19 were 

exogenous in the models while considering selected indices of stock market as endogenous. Significant value of AC was 

found to be varying in the models, at 1% for SENSEX, 5% for IN and 10% (see table 9) for FMCG while insignificant value 

have witnessed in BANK and SPICBI. In variance equations both ARCH and GARCH terms were statistically significant 

(see table 10). The value of α1 + β1 is less than 1 in all the models indicating 0.99, 0.97, 0.98, 0.96 and 0.9 and the coefficients 

of GARCH (β1) are greater than coefficients of ARCH (α1). This implies that the persistent volatility exists in the selected 

market indices. The sum of α1+β1 coefficients in the models are close to one indicating that the shock effect caused by 

COVID-19 will be persistent in the market for a long period (Shehzad et al., 2020). 

 

Table 9. The Results of Mean Equation 

 
 Variable Coefficient Std. Error z-Statistic Prob.   

Dependent Variable: SENSEX 

 C 0.3331 0.2070 1.6095 0.1075 

 AC -0.0295 0.0113 -2.6058 0.0092 

 DD 0.0111 0.0079 1.4087 0.1589 



Afzal et al., Indian Journal of Finance and Banking 10(1) (2022), 18-30 

 

24 

Dependent Variable: FMCG 

 C 0.1917 0.2370 0.8091 0.4184 

 AC -0.0234 0.0136 -1.7107 0.0871 

 DD 0.0068 0.0076 0.8905 0.3732 

Dependent Variable: BANK 

 C 0.3310 0.3457 0.9574 0.3383 

 AC -0.0403 0.0249 -1.6178 0.1057 

 DD 0.0185 0.0136 1.3663 0.1718 

Dependent Variable: SPICBI 

 C 0.0494 0.0135 3.6494 0.0003 

 AC 0.0007 0.0013 0.5752 0.5651 

 DD -0.0004 0.0004 -0.9750 0.3296 

Dependent Variable: IN 

 C 0.4687 0.2406 1.9481 0.0514 

 AC -0.0572 0.0238 -2.4058 0.0161 

 DDR 0.0063 0.0085 0.7481 0.4544 

                                                Source: Authors compilation 

 

Table 10. The Results of Variance Equation 

 
Variable Coefficient Std. Error z-Statistic Prob.   

Dependent Variable: SENSEX 

C 0.2204 0.1731 1.2733 0.2029 

RESID(-1)^2 0.2410 0.1455 1.6558 0.0978 

GARCH(-1) 0.7577 0.1202 6.3005 0.0000 

Dependent Variable: FMCG 

C 0.1801 0.1164 1.5466 0.1220 

RESID(-1)^2 0.2163 0.0947 2.2829 0.0224 

GARCH(-1) 0.7685 0.0838 9.1639 0.0000 

Dependent Variable: BANK 

C 0.3496 0.1927 1.8140 0.0697 

RESID(-1)^2 0.1785 0.0918 1.9448 0.0518 

GARCH(-1) 0.8185 0.0796 10.277 0.0000 

Dependent Variable: SPICBI 

C 0.0005 0.0005 0.8555 0.3923 

RESID(-1)^2 0.1501 0.0709 2.1153 0.0344 

GARCH(-1) 0.8188 0.0640 12.7826 0.0000 

Dependent Variable: IN 

C 0.2542 0.2211 1.1496 0.2503 

RESID(-1)^2 0.1711 0.0974 1.7561 0.0791 

GARCH(-1) 0.8052 0.0948 8.4930 0.0000 

Source: Authors compilation 

 

Post application of GARCH (1,1), the residuals of the models were subjected to test heteroscedasticity and serial 

correlation for which Ljung-Box Q and ARCH LM test have been used for the diagnosis. The P-value of all the models 

tested with both the diagnostic tests were greater than 0.05 (See table 11), signifying none of the residuals were auto-

correlated as well as heteroscedastic.  

 

Table 11. Ljung-Box Q and ARCH LM test 

 
 Ljung-Box Q ARCH LM test 

Models Q- Statistics P-Value F- Statistics P-Value 

SENSEX 0.5594 0.455 0.5320 0.467 

FMCG 0.4530 0.501 0.4294 0.513 

BANK 1.3798 0.240 1.3236 0.252 

SPICBI 1.3058 0.253 1.2514 0.266 

IN 0.7921 0.373 0.7543 0.387 

                                                          Source: Authors compilation 

 

STANDARD-VAR 

Investigating the effect of COVID-19 on selected stock indices was also observed by applying Standard Vector Auto-

Regression in this study, which is considered as a standard method in financial researches (Vo, 2017). Studies such as (Ulku 

& Ikizlerli, 2012; Patnaik et al., 2013; Usmani & Akhter, 2020) applied this technique while studying impact of foreign 

investment on Indian capital market and market returns in developing economies respectively. 

 

Table 12. VAR Lag Order Selection Criteria 

 
 Lag LogL LR FPE AIC SC HQ 

0 -634.0193 NA   0.389070  13.24529   13.64095*  13.40533 

1 -595.2306  71.24455   0.294147*   12.96389*  14.01898   13.39065* 

2 -580.4126  25.70479  0.364279  13.17169  14.88620  13.86517 

3 -553.1394   44.52770*  0.352393  13.12529  15.49924  14.08551 

    Note: *Lag order selected by criterion 



Afzal et al., Indian Journal of Finance and Banking 10(1) (2022), 18-30 

 

25 

 

There are two criteria that should be taken care of before applying VAR, firstly, the data should be stationary and 

secondly, lag length must be optimum (Gangadharan & Yoonus, 2012). The latter condition is fulfilled by obtaining  

minimum value that is based on decision criteria such as Schwarz Criterion (SC), Akaike Information Criterion (AIC), Final 

Prediction Error(FPE), Hannan–Quinn (HQ)  and Sequential Modified LR test statistic (LR) (Sahoo,2020). 

 

Market returns t = C(1)*market returns t-1 + C(2) + C(3)*AC+ C(4)*DD 

 

Table 13. Vector Autoregression Estimates 

 
 SENSEX FMCG BANK SPICBI IN 

SENSEX(-1) -0.552728 -0.499105 -0.752712 -0.000352 -0.602645 

  (0.44011)  (0.36363)  (0.58276)  (0.01636)  (0.38752) 

 [-1.25588] [-1.37258] [-1.29163] [-0.02153] [-1.55511] 

FMCG(-1)  0.152795  0.027674  0.189049 -0.000454  0.055145 

  (0.21434)  (0.17709)  (0.28381)  (0.00797)  (0.18873) 

 [ 0.71286] [ 0.15627] [ 0.66611] [-0.05702] [ 0.29219] 

BANK(-1)  0.424032  0.228459  0.543199  0.000353  0.521447 

  (0.26408)  (0.21818)  (0.34967)  (0.00982)  (0.23252) 

 [ 1.60571] [ 1.04710] [ 1.55347] [ 0.03601] [ 2.24256] 

SPICBI(-1) -0.568822  2.123704 -0.584850  0.287199  1.073163 

  (2.52888)  (2.08938)  (3.34853)  (0.09399)  (2.22671) 

 [-0.22493] [ 1.01643] [-0.17466] [ 3.05550] [ 0.48195] 

IN(-1) -0.294327 -3.58E-05 -0.163577  0.017173 -0.084803 

  (0.24800)  (0.20490)  (0.32838)  (0.00922)  (0.21837) 

 [-1.18680] [-0.00017] [-0.49813] [ 1.86303] [-0.38835] 

C  0.475989  0.113778  0.445967  0.031040  0.396357 

  (0.36198)  (0.29907)  (0.47931)  (0.01345)  (0.31873) 

 [ 1.31495] [ 0.38044] [ 0.93044] [ 2.30710] [ 1.24355] 

AC -0.073118 -0.028190 -0.098364  0.001339 -0.082027 

  (0.02579)  (0.02131)  (0.03415)  (0.00096)  (0.02271) 

 [-2.83539] [-1.32312] [-2.88068] [ 1.39738] [-3.61252] 

DD  0.005572  0.006082  0.014690 -8.06E-05  0.003543 

  (0.01134)  (0.00937)  (0.01502)  (0.00042)  (0.00999) 

 [ 0.49116] [ 0.64891] [ 0.97791] [-0.19124] [ 0.35465] 

      
R-squared  0.145929  0.122626  0.121784  0.245298  0.200285 

Adj. R-

squared 

 0.080945  0.055869  0.054964  0.187875  0.139437 

F-statistic  2.245617  1.836907  1.822554  4.271769  3.291574 

Note: Standard errors in ( ) & t-statistics in [ ] 

Source: Authors compilation 

 

Where , market returns denotes returns of different indices that includes SENSEX, FMCG, BANK, SPICBI and 

IN  at time t. wherein, C(1) denotes returns of different indices at lag 1, C(2) is constant, C(3) and C(4) are exogenous 

variables which represents the coefficients of active cases and daily death due to Covid-19 respectively. 

The lag length selection criterion was based on AIC, FPE and HQ and was found optimum at lag 1 (table 12). The 

result of VAR indicates that AC is significantly influencing only three of the selected indices, namely SENSEX, BANK and 

IN.  The t-statistics is greater than critical value of 1.96 (at 5% significance) with negative coefficients -0.073, -0.098 and -

0.082 for SENSEX, BANK and IN respectively (See table 13). Although the coefficients of DD are positive but the 

relationship have found insignificant in all the indices. The residual of the VAR should be subjected to check serial 

correlation which was not found to be serially correlated, as the P-value is> 0.05 (See table 14). Stability condition of VAR 

was also checked by using inverse root of AR characteristic polynomial method and was satisfactory, as none of the root 

was found to lie outside the circle (See Figure 1). 

 

Table 14. VAR Residual Serial Correlation LM Tests 

 
Lag LRE* stat df Prob. Rao F-stat df Prob. 

1  23.70649  25  0.5364  0.948895 (25, 309.8)  0.5370 

2  32.69350  25  0.1389  1.327316 (25, 309.8)  0.1394 

 



Afzal et al., Indian Journal of Finance and Banking 10(1) (2022), 18-30 

 

26 

 
Figure 1. Inverse Roots of AR Characteristics Polynomial 

 

Impulse Response Function 

 
Figure 2. Impulse Response Function of COVID-19 and Returns of Indices 

 

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

-1 0 1

Inverse Roots of AR Characteristic PolynomialInverse Roots of AR Characteristic Polynomial



Afzal et al., Indian Journal of Finance and Banking 10(1) (2022), 18-30 

 

27 

In the fiqure 2 of Impulse Response Function (IRF), there is one solid line between two dotted line. Former line 

shows coefficient of the impulse response while the dotted line shows bootsratpped confidence band at 90% level (Dhingra 

et al., 2016). The shock at one S.D. to AC and DD was observed for response on different maket indices (see figure 2). 

Response of SENSEX, BANK and IN to AC shows sharp decline initially but from 2nd period, it increases and remains 

negative throughout. For FMCG and SPICBI to AC, response abruptly increases to 2nd period and gradually dies out. The 

response of four indices (SENSEX, FMCG, BANK and IN) to DD shows sharp fall till 2nd period and similarly sharp rise 

in 3rd period, after which fluctuating response is seen throughout the periods. Considering response of SPICBI to DD, it 

shows decline till 3rd period and slight fluctuation during rest of the periods. 

 

DISCUSSION 
The econometric models analyzed the impact of COVID-19 on Indian stock market, which reflects the condition of Indian 

economy in short run. Overall results reveal negative impact on all the market indices influenced by COVID-19. SENSEX 

Index has been found to have negative and significant relationship with active cases of COVID-19 while applying all the 

four methods and insignificant considering daily death counts except with the results of IRF. The impact of AC on FMCG 

has evidence of significantly negative affect while observed from three methods except for IRF. Similarly, BANK has been 

negatively affected by AC shown in all the methods although GARCH predicts marginally insignificant relationship (P-

value = 0.106).The IN index shows negative and significant relationship with COVID-19 in regression, GARCH, S-VAR 

and IRF. 

Interestingly, SPICBI is showing positive relationship influenced by COVID-19. Yet only regression concludes on 

significant coefficients while GARCH and S-VAR show insignificant but positive relationships. Positive influence can also 

be seen in IRF graph.  

 

CONCLUSIONS 

As the COVID-19 pandemic is causing significant disruption in the World economy (Shehzad et al., 2020), it is also highly 

likely for the Indian economy to enter into phase of “economic shock” (Baldwin and di Mauro, 2020). The econometric 

models analyzed the impact of COVID-19 on Indian stock market, which reflects the condition of Indian economy in the 

short run. GARCH (1,1); Standard Vector Auto-regression and IRF was applied to further investigate the hypotheses.  

The result for SENSEX and COVID-19 is in line with the recent plunge of SENSEX in end week of March 2020 

(Business Reporter, 23 March 2020). The negative relationship may have been caused due to withdrawal of investment from 

the market by both domestic and foreign investors; this is evident by Foreign portfolio investors (FPI) pulling INR 1 Lakh 

crore from Indian market (Sharma, March 2020).  

The negative impact of COVID-19 on Index of FMCG, Banking and Industrial sectors may have been due to the 

investor’s fear and loss of trust in these sectors, which corroborates with recent survey by Babar (The Economic Times, 16 

April 2020). Although, the healthcare and IT sector, are predicted to have positive relation in near future (Khanna, 15 April 

2020; The Hindu Business Line, 10 April 2020), the result depicts otherwise. 

COVID-19 and S&P BSE India Corporate Bond Index is positive due to the fact that corporate bonds are fixed 

income securities which are less volatile as compared to equities. This may have caused investors of corporate bonds not 

withdrawing their investment to that scale in contrast to other indices. Additionally due to less volatility, this causality can 

be attributed to the possible influx of more investment in corporate bonds, when it is considered the government should 

channelize investment which will help in reviving the real economy (Sen, 2019).  

Overall, based on this study, the effect of pandemic with other indices suggests many insights and implications. 

First, investors can diversify portfolio to mitigate the risk caused by the ongoing pandemic. This can be achieved by 

consciously including corporate bond in their investment portfolio. Second, the policy makers of secondary market should 

emphasize development of debt based securities. Third, the study implies that government should facilitate the investors to 

put money into bond and related stocks. 

This shall help the stock market to revive by the influx of investment and in the time of pandemic, this strategy can 

act as a buffer to the forecasted economic shock. Fourth, the bond market which has been a concern for the government, 

being sluggish for a long time will also be boosted. Fifth, this study will not only add knowledge to the existing literature 

but also will facilitate the academician and researchers to comprehend the impact of pandemics on Indian economy. Lastly, 

this study suggests that the government should take preventive measures against devastating effect of COVID-19. 

 

 

 

Author Contributions: Conceptualization, M.A.A., N.K., A.S.M., and M.T.; Data Curation, M.A.A., N.K., A.S.M., and M.T.; Methodology, M.A.A.; 

Validation, M.A.A.; Visualization, M.A.A.; Formal Analysis, M.A.A., N.K., A.S.M., and M.T.; Investigation, M.A.A., N.K., A.S.M., and M.T.; Resources, 
M.A.A., N.K., A.S.M., and M.T.; Writing – Original Draft, M.A.A.; Writing – Review & Editing, M.A.A., N.K., A.S.M., and M.T.; Supervision, M.A.A.; 

Software, M.A.A.; Project Administration, M.A.A.; Funding Acquisition, M.A.A., N.K., A.S.M., and M.T. Authors have read and agreed to the published 

version of the manuscript. 

Institutional Review Board Statement: Ethical review and approval were waived for this study, due to that the research does not deal with vulnerable 

groups or sensitive issues. 

Funding: The authors received no direct funding for this research. 

Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. 

Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available 

due to restrictions. 

Conflicts of Interest: The authors declare no conflict of interest.  



Afzal et al., Indian Journal of Finance and Banking 10(1) (2022), 18-30 

 

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