The Impact of COVID-19 on the Volatility of Bangladeshi Stock Market: Evidence from GJR-GARCH Model

  • GOLDER, Uttam (Department of Finance and Banking, Jashore University of Science and Technology) ;
  • RUMALY, Nishat (Department of Finance and Banking, Jashore University of Science and Technology) ;
  • SHAHRIAR, A.H.M. (Department of Finance and Banking, Jashore University of Science and Technology) ;
  • ALAM, Mohammad Jahangir (Department of Accounting and Information Systems, Jashore University of Science and Technology) ;
  • BISWAS, Al Amin (Department of Finance and Banking, Jashore University of Science and Technology) ;
  • ISLAM, Mohammad Nazrul (Department of Finance and Banking, Jashore University of Science and Technology)
  • Received : 2021.12.15
  • Accepted : 2022.03.07
  • Published : 2022.04.30


The enormous sway of COVID-19 on the international financial market has been felt across the globe. The financial markets of Bangladesh have also been similarly affected by the global epidemic and experienced a significant increase in volatility. To scrutinise the connection between COVID-19 and the Dhaka Stock Exchange (DSE) indices' return and instability, this study uses data of the DSE from February 2014 to September 2021. A comparative examination of the return and instability of the stock indices of the DSE has also been done considering the outbreak of the current COVID-19 situation. After using the GJR-GARCH (1,1) model, this review uncovers that the outbreak of COVID-19 has a statistically positive noteworthy association with the DSE stock indices' instability, which increases the market's volatility. Traders' fear and the rising frequency of COVID-19 reported patients could cause this. Besides, according to this study, COVID-19 shows a substantial positive linkage with stock market returns that increases the market's return. An appealing valuation, lower interest rates in the banking channel, economic rebound following the closure to prevent coronavirus transmission, improved remittance inflows, and a return of export revenues could all have contributed to this outcome. In addition, the findings also reveal that all market indices are in a mean-reverting phase.


1. Introduction

The infectious COVID-19 virus was first found in Wuhan, China, in 2019, and from here it spread all across the globe. The unexpected spread of the SARS- COV has endangered the entire world. It has created an enormous problem for public health and the economy (Bora & Basistha, 2021; Khan et al., 2020; Wang & Han, 2021). The hazard faced by the general public due to this pandemic has been devastating because of lockdowns placed in many countries. It has led to an unparalleled pitfall for people stretching beyond health (Naseem et al., 2021). People started to think differently regarding their occupation and working methods (Kramer & Kramer, 2020). Teresiene et al. (2021) alluded to this pandemic as different from other financial crises that occurred worldwide. To defend public health, many countries took various policies, but some of those created unemployment problems because all people do not have the opportunity to earn a living from their homes (Jiang et al., 2020). The footprints of this pandemic have affected every nook of economy, business decision, and investment programs across the world. The sickly financial activities have made many countries’ stock markets volatile and bearish in this pandemic (Alzyadat & Asfoura, 2021; Chaudhary et al., 2020). Volatility, a statistical measure of financial risk, helps identify the dispersion of portfolios returns or market indices. Knowing about the level of vulnerability that occurred due to any pandemic situation is crucial for investors to optimise wealth.

Most of the largest GDP holding courtiers like Spain, Italy, and the UK suffered extremely due to their dependence on productivity and the labour market (Fana et al., 2020). Well-developed countries became puzzled to handle the catastrophe that arrived due to COVID-19, where Bangladesh, as a poverty-stricken country, went through a teribble time (Al-Zaman, 2020). Being an overpopulated and poverty-stricken country, Bangladesh faces tremendous hazards because of unpreparedness to manage this pandemic (Kabir et al., 2021). Analysts have notified that volatility in the equity market is connected to anxiety because it is the major indicator to make investment decisions (Kayser & Golder, 2019). The higher variation in share indices notifies greater changes in stock price, leading to an uncertain market (Chaudhary et al., 2020). Some important reasons for making the share market unpredictable and vague include travel restrictions, maintenance of lockdown, shut down of various industries and manufacturing companies, lower focus on this market development, etc. The worst affected sectors were manufacturing factories, tourism, local retail shops, and the investment market (Dube et al., 2021; Pjanić, 2019).

World Health Organization acknowledged the coronavirus as a universal epidemic on March 11, 2020. Even much before this announcement was made, the stock indices of different countries started to fall greatly; for example, Dow Jones Industrial Average (DJIA) Index reduced 26% within four trading days of the declaration of COVID-19 as an epidemic (Mazur et al., 2021). Moreover, ASX-200 plunged 1.9%, Asia Dow Index fell 4%, and Nikkei-225 of Japan lost by 3.6% (Mishra & Mishra, 2020). However, despite the Coronavirus pandemic, very interestingly, the stock market of Bangladesh started to step up after seven to eight years. DSEX, the broad index of DSE, became the best performing index from July to September of 2020, according to the research study conducted by Asia Frontier Capital (Hugger, 2021). However, at this time, some major decisions were taken by Bangladesh Securities and Exchange Commission (BSEC). In 2020, the controlling authority of BSEC was reformed, and the head of the commission, along with other parties, reformed the share market to a large extent by changing different ongoing rules and implementing those through reformative steps (Zahid, 2021). According to this premise, it was expected that people will get back their motivation to invest in the share market (Habib, 2021). The regenerative policies and diligence revealed numerous positive changes in the share market. Besides, limited opportunities for individual investors in other sectors and lower depository interest from banks and other financial institutions also encouraged people to invest in the equity market (Mavis, 2021).

Some positive motivations lead this study to go forward. In Bangladesh, the number of studies on the volatility modelling during COVID-19 on the equity market is exiguous. This work explores the volatility level of the share market in Bangladesh at the stage in the COVID-19. Likewise, it covers all of the stock indices of DSE to have a clear thought regarding the waves of this fatal virus on the overall stock market. This study reveals how historical and current stock prices have made the capital market volatile throughout COVID-19. It also scrutinises the presence and nature of asymmetric information or leverage effect on this market.

OTGHEU_2022_v9n4_29_f0001.png 이미지

Figure 1: Daily Percentage Change of DSEX, DSES, and DS30 (February 2014 – September 2021)

Moreover, it provides a clear idea about the three DSE indices’ mean-reverting capacity. Besides, throughout this study, a comparative overview is achieved through which it is possible to know how significantly COVID-19 makes a particular index volatile. Finally, it discloses whether the present and past days related to COVID-19 impacts the index through a positive or negative shock.

To the authors’ best acquaintance, it is the first-ever comprehensive investigation regarding the consequences of COVID-19 on the instability of the stock exchange in Bangladesh. Another notable gap is that most previous studies were based on a theoretical perspective. No other studies have been conducted earlier for volatility measurement of the stock market to realise the cruel claw of Coronavirus disease in Bangladesh.

2. Literature Review

In business, finance, and investment, the capacity to forecast and pretend volatility is a significant virtue. Staying in an integrated world, various sectors have to struggle with lots of externalities and spillover effects from time to time. The coronavirus pandemic has affected various business activities, including roadside restaurants, tea stalls, shopping centres, physical bookstores etc. (Nguyen et al., 2020; Pjanić, 2019). Stakeholders of share markets are very worried about the volatility of this market in this current pandemic of COVID-19 because it is unlike any prior financial catastrophes (Fernandes, 2020). Barua and Barua (2020) reported that the COVID-19 pandemic generates many-sided catastrophes for the banking sector through widening default ratios, mostly in developing economies (Rizwan et al., 2020). Authors worldwide are working on this ongoing issue because people may face unforeseen problems in the future, which may cause market volatility, and proper research may find a solution to eliminate the unavoidable externalities. Knowing about the volatility in markets is crucial, but it is quite tough to observe because volatility is both capricious and sensitive at the same time (Awalludin et al., 2018).

Many researchers and econometricians have generated different volatility models to realise the level of variation in the stock market. Every investor and related party requires a clear idea regarding the volatility in stock markets to grab benefits. Yong et al. (2021) revealed the significance of volatility in the pricing of assets, supervision of risk, choosing and allocation of the portfolio, and mentioned the importance of the GARCH model in determining these kinds of volatility. Chaudhary et al. (2020) analysed indices of the share market of 10 renowned countries grounded on GDP to know the consequences of coronavirus on the share market. The research paper revealed that in the COVID phase from January 2020 to June 2020, there was an adverse effect on all market indices taken for the study. Although in the second quarter of the pandemic, all market indices represented signs of slight recovery with different strengths, the level of volatility remained higher. In recent times, a study was undertaken by Yong et al. (2021), who also mentioned COVID-19 as a concerning matter for business analysts and econometricians. The central point for this anxiety was the volatility in portfolio selection, return from preferred investments and pricing of assets.

To control the sudden terrible effect of the pandemic, many countries resorted to strict lockdowns due to coronavirus, a contagious disease. A countrywide lockdown policy is intended to control the spread of this highly infectious disease (Ashraf, 2020; Utomo & Hanggraeni, 2021). Although the decision reduces the impact of the disease to a limited extent, the decision created a direct impact on the economy, and several authors supported his concept (Chaudhary et al., 2020; Mishra & Mishra, 2020; Ozili & Arun, 2020). Governments of different countries uphold various regulations that ultimately affect the stock market to a certain extent (Topcu & Gulal, 2020). Deb (2021) scrutinised the share price movements of three airlines companies and tried to forecast the price volatility of the share market using a modified GARCH model. The outcome showed that the pandemic had an exotic impact on stock prices. Mishra and Mishra (2020) disclosed COVID-19 as a universal contagion and declared that the unwanted demand and supply-side occurrences forced the economics to slow down their outlooks for the future. This study also revealed that the pandemic brought about anxiety in markets by reducing investors’ strengths and inducing market volatility. The market’s volatility varied in its impact depending on the severity of the catastrophe. Albulescu (2021) evaluated both the global affected rate of coronavirus and the rate in the USA and used the Standard and Poor 500 index to measure volatility. The review disclosed that COVID-19 is an alarming source of volatility in financial activities, making risk management activities vulnerable.

Bora and Basistha (2021) compared India’s stock market in the earlier COVID-19 and after the COVID period. Utilising the GARCH model, this work revealed that the Coronavirus pandemic negatively impacted the Indian stock market, where volatility was high, and the return on indices was lower at the first lockdown in India. In this vein, Ozili and Arun (2020) conducted a study on the economy of four renowned continents revealed that 30 days of social distancing affected the condition of the stock exchange.

Duttilo et al. (2021) examined the indices of two euro zone countries by focusing on time-varying risk premium and the effect of leverage. This review paper revealed that the mid-large financial centres of euro areas suffered more during the first wave of Coronavirus disease. The second wave affected the stock market of Belgium significantly. The relationship between Coronavirus disease and uncertainty between stock return and price instability in the USA in 1st January 2019 and 30th June, 2020 was scrutinised by Hong et al. (2021), and this study disclosed that this pandemic generated inefficiency in market inducing income and wealth imparity among participants where there were lots of liquid money on hands but a shortage of available funds. Using an event study and employing panel regression, Sun et al. (2021) appraised China’s stock market and detected that stocks having distinct features from different industries were affected differently. People’s distinct behaviours and sentiments also affected the share markets, where affluent investors were more ready to take risks (Chiah & Zhong, 2020). Production, technological, and pharmaceutical sectors faced low volatility, but transportation, industry, and mining sectors were less resilient to contagious disease (He et al., 2020).

The empirical investigation by Bora and Basistha (2021) uncovered that the profit before the epidemic on the indices named BSE and NSE of India was more remarkable than the post COVID era. The stock exchange faced instability at the time of the spread of the virus. Every sector, including the share market and financial system in India, was affected due to this pandemic. The authors mentioned that some extraordinary policies and programs of liquidity injection were foremost necessities in that time to step down the breakneck clutch of coronavirus. Dube et al. (2021) also analysed the impact of the coronavirus disease. They suggested fruitful ways to overcome the situation and proposed numerous recovery pathways to lessen the undesirable influences of this pandemic.

Share market is a vast area where sudden events and usual religious occasions or festivals are influenced. Recently, public news or affairs have greatly affected the trade decision in the stock market (Cepoi, 2020). Financial and business literature reveals that the impact of the calendar, behavioural effect, and festival occasions also predict stock markets. In this premise, Hassan and Kayser (2019) scrutinised the share market to know the impact of the most important religious festival Ramadan and disclosed that the festival harmed the DSE trade volume because of decreased banking hours and religious thoughts of the investors. Haque and Chowdhury (2020) examined DSE and CSE to compare the effect of coronavirus by investigating available market data before and after the pandemic. The research work also scrutinised the aftermath of the policy interventions to the market. The authors revealed that Bangladesh is the only country that kept the stock market closed for about 66 days. However, regulatory bodies took numerous steps, including reshuffling the governing committee of the share market and introducing floor prices.

3. Data and Methodology

The analysis was carried out with daily pricing data obtained from the Dhaka Stock Exchange (DSE) website (DSE, 2021), which included the Dhaka Stock Exchange Broad index (DSEX), Sharia index (DSES), and Blue-chip index (DS30). The investigation was conducted between February 2014 to September 2021. The sampling of this time frame included both the pre-COVID era (February 2014 to February 2020) and the post-COVID era (March 2020 to September 2021). Institute of Epidemiology, Disease Control, and Research (IEDCR), a government institution of Bangladesh, has provided the data for the Covid-19 positive persons. The IEDCR confirmed the first COVID-19 patient on 8th March 2020; the trading day before 8th March was classified as pre-COVID period; the COVID-19 period was confirmed after 8th March. Following the natural log difference technique (Chaudhary et al., 2020; Duttilo et al., 2021), all market indices’ returns were estimated employing the formula below.

\(R_{k, t}=\ln \left(\frac{P_{k, t}}{P_{k, t-1}}\right)\)       (1)

Where, everyday return on index k at period t is Rk, t. The day-to-day ending value of index k at period t is Pk, t, and Pk, t−1 is the everyday concluding value of index k at period t−1. If a movement in period does not alter the mean, variance, and autocorrelation structure, the time series is stationary (Golder et al., 2020). This study progresses in the subsequent phases. In the first step, the unit root testing is employed to assess if a data set is stationary. Academicians use relevant approaches for verifying the stationarity of time series data to authenticate the regression results. Here, data stationarity is verified using two universally known unit root screen techniques: the Augmented Dickey-Fuller (Dickey & Fuller, 1981) and Phillips-Perron (Phillips & Perron, 1988) unit root checks, and the findings of both scores are then cross-validated to show the integrating phase. For identifying volatility, in the second phase, the Autoregressive conditional heteroskedasticity–Lagrange Multiplier test (ARCH–LM) (Engle, 1982) is engaged in evaluating time series for heteroscedasticity and in assessing for the existence of ARCH/GARCH consequence. The Generalised Autoregressive Conditional Heteroskedasticity (GARCH) family approach is used, in the third step, to forecast the volatility of Dhaka Stock Exchange indices.

Bollerslev introduced the GARCH model in 1986 as an expansion of the ARCH model (Bollerslev, 1986), and it is convenient for capturing the issue of volatility clusters. However, a wide range of activities, such as mergers and acquisitions or terror acts or the introduction of new technology, can significantly impact the decision-making process of financial speculators, which in turn can have a significant asymmetric impact on financial markets. A basic ARCH/GARCH strategy considers both poor (negative) and fantastic (positive) events symmetrically and their influence on price fluctuation similarly; as a result, a severe favourable or unfavourable shock may have the identical size and scope in the series’ volatility. Generally, when stock markets receive positive news, the price settles down into a condition of calmness, and volatility lessens, and vice versa. This review uses the GJR-GARCH model, which examines the asymmetric reaction of conditional instability to news, to address the restrictions of the standard GARCH model, which enforces symmetric instability feedback to constructive and undesirable innovations (Glosten et al., 1993; Zakoian, 1994).

However, in the case of leverage consequence, an additional theatrical aspect of financial data is that negative yields (price drops) incline to enhance instability by a bigger proportion than constructive yields (price rises) of the identical scale. The GJR-GARCH model is also able to explore the impact of leverage effect (Duttilo et al., 2021). So, the GJR-GARCH model is adapted in this investigation to find the conditional volatility, and one exogenous dummy variable is taken in the model’s conditional mean and conditional volatility equations to expose the consequence of the COVID-19 pandemic. The GJR-GARCH (1, 1) model with external dummy variable is stated below:

Conditional Mean Equation:

\(R_{k, t}=\Psi+\vartheta_{1} R_{k, t-1}+\theta_{1} \text { COVID }-19_{t}+\varepsilon_{k, t}\)       (2)

Conditional Volatility Equation

\(\sigma_{k, t}=\Omega+\varphi_{1} \sigma_{k, t-1}+\tau_{1} \varepsilon_{t-1}^{2}+\omega_{1} I_{t-1} \varepsilon_{t-1}^{2}+\rho_{1} \text { COVID-19 } t\)       (3)

\(\text { Here, } I_{t-1}=\left\{\begin{array}{l} 1 \text { if } \varepsilon_{t-1}<0 \text { bad news } \\ 0 \text { if } \varepsilon_{t-1} \geq 0 \text { good news } \end{array}\right.\)

Here, Rk, t and εk, t specify the return and residual of stock index k at period t, consecutively in the conditional mean equation. Ψ represents the constant, Rk, t−1 and COVID-19t is the one-period lag of return of stock index k at period t−1, and dummy variable for COVID-19 epidemic at time t. Besides, ϑ1 and θ1 are coefficients of Rk, t−1 and COVID-19t, respectively. In conditional volatility equation σk, t signifies the conditional standard deviation of stock index k at period t, and is the constant. τ1, φ1, ω1, and ρ1 are coefficients of ARCH, GARCH, leverage, and a dummy variable for the COVID-19 pandemic. τ1, and φ1 are non-negative parameters must have to be significant and represent the ARCH and GARCH effect, respectively. A higher value of the ARCH term represents greater responsiveness to new facts, and greater GARCH values represent a greater sum of timeframe for the modification to disappear. However, if the value of (τ1, + φ1) closes to 1; it indicates the existence of a higher level of volatility (Chaudhary et al., 2020; Rastogi, 2014). ω1 is the asymmetric or leverage contribution, which states that undesirable innovations have a greater consequence than optimistic shocks, and in order to capture the effect of leverage, ω1 must have to be greater than 0. However, the positive news (εt−1 ≥ 0), and the negative news (εt−1 < 0) makes an impact on the conditional volatility equation and the positive incident influences of φ1, while the adverse event has a consequence of τ1 + ω1. In the presence of a positive ω1, negative shocks increase fluctuation more than positive ones, while a value of 0 signifies that the shocks’ effects are symmetric. Besides, in the GJR GARCH model, τ1 + ω1 ≥ 0. The COVID-19 dummy catches the value of 0 during the pre-COVID-19 era (starts from February 2014 - February 2020) and 1 during the COVID-19 stage (starts from March 2020-September 2021). The presence of a constructive and statistically meaningful coefficient for COVID-19 in the conditional mean equation suggests that COVID-19 and an upsurge in the market’s yield are linked. Conversely, if the incidence of a negative and statistically meaningful coefficient for COVID-19 in the conditional mean equation appears, it advocates that COVID-19 and a downturn in the market’s yield are linked. In the conditional volatility equation, COVID-19 may be linked to an escalation in market fluctuation if the coefficient of COVID-19 is positive and statistically noteworthy. However, COVID-19 and decreased market volatility may be linked if the COVID-19 coefficient is negative and statistically meaningful. However, In the fourth steps, some diagnostic tests are included to validate the results.

4. Results and Findings

The mean returns of all the indices of the entire sample period express positive outcomes, while in the pre- COVID-19 era, DSEX and DS30 indicate negative mean returns, confronting the positive mean return of DSES (Table 1). During the COVID-19 stage, the mean returns of all indices considerably provide positive results. Thus, DSES reveals significantly positive mean results in all three periods, indicating strong positive market reaction during the pre-crisis, crisis, and mixed periods, proving that the Shariah index unveils more excellent performance than the conventional one due to investors’ ethical and religious beliefs (Aarif et al., 2021).

Table 1: Descriptive Statistics

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During the COVID-19 era and entire sample span, all indices of the Dhaka Stock Exchange provide positive median values, opposing negative median values during the pre-COVID-19 era for DSES and DS30 indices. All the indices have the highest maximum values and lower minimum values during the whole sample timeframe and COVID-19 era. In the COVID-19 time, the minimum values of indices range from –7.243397 (DSES) to –6.394609 (DS30).

Although the standard deviations for all indices during the COVID-19 stage are slightly higher than those of other periods, all indices’ higher positive mean returns during the COVID-19 stage show greater return in the capital market. All the indices during COVID-19 have lower skewness values than the pre-COVID-19 and entire sample periods. On the other hand, kurtosis values are higher in the time of the COVID-19 stage than the pre-COVID-19 stage, though, during the entire sample frame, the kurtosis values remain higher than those of other periods, indicating high chances of losses (Chaudhary et al., 2020).

Table 2 includes the required prerequisites for stationarity and ARCH effect testing. This study employed two different tests: ADF and PP unit root testing, and both of which looked for stationarity in the return of the three indices, namely, DSEX, DSES, and DS30, of Dhaka Stock Exchange (DSE). Besides, the ARCH-LM test is employed to see if the residual variance is unequal across a span of observed values. Estimations of both ADF and PP unit root assessment designate that all of these variables (DSEX, DSES, and DS30) are stationary at the level both in constant, and constant plus trend, and this testifies that when a movement in period happens, it does not change the structure of the mean, variance, and autocorrelation of the return series. Accordingly, the null hypothesis of no ARCH effect is disproved by LM statistical results, and an ARCH effect is founded in the time series models’ residuals, supporting GARCH model applicability.

Table 2: Unit Root and ARCH-LM Test

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Note: cL = with constant at level, ctL = with constant and trend at level, ***denotes significant effect at 1% level.

GJR-GARCH (1, 1) findings for all stock indices are shown in Table 3. The results are divided into three parts. Part A of Table 3 represents the outcomes of conditional mean equations of the three models, where the coefficient Y is not statistically significant in any indices of DSE, and in all cases, the past value of returns significantly predict the current series positively. However, the past value of returns of DSEX has a more significant impact in predicting the current return series than the other two indices, namely, DSES and DS30. Besides, the COVID-19 impacts all the indices of DSE, increasing the market return. Economic expansion after the COVID-19 closure, higher remittances, and restoration of export earnings have contributed to this result (Hugger, 2021).

Table 3 (Part B) represents the outcomes of the conditional volatility equation, and here, the coefficients of the constant, ARCH, GARCH, leverage, and COVID-19 are positive and statistically noteworthy for all the indices. The coefficients of ARCH (τ1), GARCH (φ1), and leverage (ω1) terms in conditional volatility equations are related to information or news. The ARCH term represents the most recent information, and its statistical significance reveals that the current news has influenced the instability of the stock market. The ARCH term is a statistically significant indicator of current market instability, indicating that current news affects the stock market. It is worth noticing that the Broad index of the DSE (DSEX) has the most considerable ARCH effect, while the Blue-chip index (DS30) has the lowest, and the ARCH effect of the Sharia index (DSES) is in the second position in the case of ARCH effect. So, the DSEX is mainly influenced by current news, followed by DSES, and DS30, respectively.

Table 3: GJR-GARCH (1,1) Model with COVID-19 Dummy Variables

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Note: Records in ( ) specify the value of Z-statistics. ***, **, and * denote significant effect at 1%, 5%, and 10% level, consecutively.

Besides, there is a statistical significance to the GARCH term, which implies that past information influences market volatility. If the GARCH coefficient is sizable, it indicates continuous volatility and indicates that shock to conditional variance takes a prolonged period to dissipate. Thus, the outcome reveals that shocks to conditional variance require the most prolonged period to diminish in the context of the Blue-chip index (DS30), followed by the Sharia index (DSES) and the Broad index of the DSE (DSEX).

Any shock may have continuing consequences on the predicted values of the conditional variance if the coefficients of ARCH and GARCH terms are jointly near to one. It indicates that any shock may have long-lasting effects on , which indicates that conditional variance is durable. The result (Table 3, Part C) indicates that if there is any shock in the Blue-chip index (DS30), it has the most long-lasting effects on conditional volatility, followed by the Sharia index (DSES) and the Broad index of the DSE (DSEX). However, if the value is less than one, it indicates the mean-reverting process. From part C of Table 3, the Broad index of DSE (DSEX) has the highest mean-reverting process, while the Blue-chip index (DS30) has the slowest mean-reversion capacity. However, as for mean-reversion capacity, the Sharia index (DSES) falls somewhere in the middle of the other two indices.

Since the leverage term verifies the asymmetric signal on stock return and advises that the adverse shock story appears to rise volatility more than favourable shock news, meaning that negative shock story leads to more instability than positive shock stories in the marketplace. The outcomes of Table 3, part B, reveal that the Blue-chip index (DS30) has the lowest asymmetric impact following the DSE Broad index (DSEX). However, the Sharia index (DSES) is in the middle of the two other indices affecting the leverage effect.

The COVID-19 variable is included in the conditional volatility equations using the GJR-GARCH (1, 1) model. The findings of Table 3, part B, show that the infection has considerable positive consequences on the conditional variance for all indices, revealing that COVID-19 exacerbated market instability in all indices. The infection of COVID-19 intensified market volatility most in the Sharia index (DSES) following the Blue-chip index (DS30). However, the DSE Broad index (DSEX) is least affected by the coronavirus, and this outcome is consistent with Chaudhary et al. (2020), Duttilo et al. (2021), and Yong et al. (2021).

Table 3 (Part C) reports the result of the diagnostic test of this GJR-GARCH model, where the ARCH-LM test is used to identify heteroskedasticity in the square of standardised residuals of the model. The outcome implies that the models are homoskedastic, as all three models are executed suitably in this investigation.

5. Conclusion

The consequence of COVID-19 on the Dhaka Stock Exchange’s (DSE) performance, Bangladesh’s largest stock market, is investigated in this review. This study employs the GJR-GARCH (1, 1) model with dummy variable taking the daily return of three indices, namely, DSE Broad index (DSEX), Sharia index (DSES), and Blue-chip index (DS30) from February 2014 – September 2021. The outcomes disclose that the shock of COVID-19 outbreaks substantially influences the volatility and return of all stock indices of DSE. All the indices show volatility in their respective conditional volatility equations. The COVID-19 infection demonstrates a substantial positive impression on conditional volatility equations, which increases the market’s volatility in all indices. However, it increases volatility most in the Sharia index (DSES) following the Blue-chip index (DS30) and the DSE Broad index (DSEX). The study reveals that current events affect the stock market of Bangladesh, and the DSE Broad index (DSEX) has an enormous ARCH effect, followed by the Sharia index (DSES) and Blue chip index (DS30). The result shows that disruptions to conditional variance take the maximum duration to decline in the setting of the Blue-chip index (DS30), followed by the Sharia index (DSES) and the Broad DSE index (DSEX). Besides, the Blue-chip index (DS30), followed by the DSE Broad index (DSEX), and the Sharia index (DSES), has the lowest asymmetric impact. Moreover, the study also finds a considerable constructive influence of COVID-19 on the conditional mean equations in all indices of DSE. Economic recovery following the shutdown to avoid the spread of COVID-19, increased remittances, and a resumption of export income may have led to this consequence (Hugger, 2021).

Here are some significant consequences, e.g., profitable possibilities for traders and speculators can be found in market inefficiencies (Golder et al., 2019), which may be exacerbated by COVID-19. In order to get the most out of the investment, rational investors should have an eye for insider trading. As a result of a crisis, income and economic disparity can widen because investors with a large amount of liquidity may go to the capital market for opportunities to make money.

However, this model does not account for macroeconomic parameters like GDP, stock market capitalisation, interest rate, exchange rate, oil price, etc. Thus, this argument seems intuitive. Future studies should prudently reflect the latent belongings of macroeconomic variables to gather more knowledge in the field. Furthermore, these findings only apply to the Dhaka Stock Exchange’s indices (DSE). It is possible that extending this research to include Bangladesh’s second major stock exchange, the Chittagong Stock Exchange (CSE), and studying the nature of connections between COVID-19, and CSE might provide different conclusions.


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