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. 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