Short Term Stress of Covid-19 On World Major Stock Indices
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Short Term Stress of COVID-19 on World MajorStock Indices
Muhammad Rehan ( [email protected] )Gaziosmanpasa University https://orcid.org/0000-0001-5056-5307Jahanzaib Alvi Iqra University https://orcid.org/0000-0001-9145-6545Süleyman Serdar KARACA, Gaziosmanpasa UniversityResearch ArticleKeywords: COVID-19, SARS, Pandemic, Financial Market, World Stock Exchange.DOI: https://doi.org/10.21203/rs.3.rs-49510/v1License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License age 2/35
Abstract
The main objective of this study is to check short term stress of COVID-19 on the American, European,Asian, and Paci c stock market indices, furthermore, the correlation between all the stock markets duringthe pandemic. Secondary data of 41 stock exchange from 32 countries have been collected frominvesting.com website from 1 st July 2019 to 14 th May 2020 for the stock market and the COVID-19 datahas been collected according to the rst cases reported in the country, stocks market are classi ed eitherdeveloped or emerging economy, further divided according to the subcontinent i.e. America, Europe, andPaci c/Asia, the main focus in the data is the report of rst COVID-19 cases. The study reveals that thereis volatility in the all the 41 stock market (American, Europe, Asia, and Paci c) after reporting of the rstcase and volatility increase with the increase of COVID-19 cases, moreover, there is a signi cant negativerelationship between the number of COVID-19 cases and 41 major stock indices of American, Europe,Asia and Paci c, European subcontinent market found more effected from the COVID-19 than anothersubcontinent, there is Clustering effect of COVID-19 on all the stock market except American's stockmarket due to smart capital investing.
1. Introduction
Coronavirus is known as COVID-19 which affects the Wuhan, China in December 2019 and became thecause of the crisis in Hubei China and then for the rest of the world, all of sudden COVID-19 became aglobal pandemic. The entire world faced volatility in the stock market and a signi cant decline in theequity market. This is the biggest volatility level seen in the United State stock market after October 1987and December 2008. (Baker, S., Bloom, N., Davis, S. J., Kost, K., Sammon, M., & Viratyosin, T. 2020)The WHO (World Health Organization) o cially declared a global pandemic to the COVID-19 outbreak on11 th March 2020. According to the WHO number of con rmed cases has been reached up to 4 307 287on 14 th May 2020 and it continues increasing day by day, Further as per reported by the WHO 216countries has been affected by this pandemic and most number of con rmed cases has been reported inthe USA with 1,361,522 con rmed cases (WHO). The COVID-19 has signi cant effect the world economyin short term as well as in the long term, in shorter-term consequence is limited activity in the economydue to strict lockdown, in the longer-term impact of the COVID-19 many small businesses will be closedand unemployment will be increased, the number of industries will suffer i.e. tourism airlines and hotels(Zhang, D., Hu, M., & Ji, Q. 2020).The question of this research is addressed the predominant importance equally for the marketpolicymaker, institutional investors, and individual investors. 12% decrease has been recorded in the DowJones Industrial Average index which was the 2 nd highest decline in the market history since 124 years on16 th of March 2020, although US government has taken many actions intended to improve the market age 3/35 which includes economy relief program and scal stimulus package the market did not improve(Gormsen & Koijen 2020).This paper aims to study the short term stress of COVID-19 on the performance of the major stock indicesof the 32 countries. In this research to recognize the systemic risk pattern in the stock market we aregoing to answer the following questions through available data, How will we react to the stock marketduring this pandemic? Does systemic risk escalate all over the world? Do the clustering effect exits in thestock market return?The structure of this paper is as follows. Section 2 consists of a literature review. Section 3 methodology.Section 4 results and nancings, Section 5 discussion and conclusion.
2. Literature Review
After the outbreak of the COVID-19 so far research paper has been published about the impact of COVID-19 on the stock market and the world economy before this pandemic there is literature available on theAsian u 1957 and SARS 2003 which shows that the negative impact on the stock market all over theworld.Barro, R., Ursua, J., & Weng, J. (2020) compared the Spanish Flu losses with COVID-19 and predict theconsequences of COVID-19 on the world economy, further they that the COVID-19 has caused to marketcrashes, volatility, decline in the interest rate and decrease in the economic activities. The research usedforecasting model to identify future dividend and its growth, results reveal that the signi cant decrease inthe dividend growth 16% and 23% in the USA and Europe respectively and decline in the growth of GDPexpected to 3.6% and 5% in the USA and Europe respectively till 12 th of May, 2020, further result revealthat within the two years of period expected dividend growth will -29% and -38% in the USA and Europerespectively (Gormsen, N. J., & Koijen, R. S. 2020).Cajner, T., Crane, L. D., Decker, R. A., Hamins-Puertolas, A., & Kurz, C. (2020) researched the developmentsthe labor market during COVID-19, the results show that 13 million people lost their jobs in just two weeksfrom 14 March to 28 March 2020 in the USA, further they compared that 9 million jobs people lost theirjobs in the Great Recession in the USA, moreover most effected sector from the COVID-19 is hospitality30% decline in the employment which is almost 4 million people lost their jobs. Humphries, J. E., Neilson,C., & Ulyssea, G. (2020) surveyed 8 thousand small business owners USA based and they show mainthree ndings in their research, the rst one is that the 60% of the business owners already red oneworker from the job as they don’t aware with the CARES act by USA government which is also 2 nd ndingof the research, and the third one is 46% of the business owner that the COVID-19 negative impact willremain for next two years.Bartik, A. W., Bertrand, M., Cullen, Z. B., Glaeser, E. L., Luca, M., & Stanton, C. T. (2020) has surveyed 5,800owners of the USA based small business and reveal some important nding that the 43% of the businesshas been stopped their activities which means they closed their business temporary and the 40% age 4/35 decreased seen in the employment rate, the number of business has been disabled nancially andnumbers of business are awaited for the Government aids.Alfaro, L., Chari, A., Greenland, A. N., & Schott, P. K. (2020) researched the change in the market return dueto COVID-19 and research shows that 4% to 11 % signi cantly decline has been seen in the total marketvalue and further they nd that the increase in the number of cases causes a decrease in the volatility ofthe market returns.Zhang, D., Hu, M., & Ji, Q. (2020) research about the impact of COVID-19 on world nancial markets, theyargue that the due to COVID-19 record level of risk increase in the market which affected the investors invery limited time. Onali, E. (2020) investigate the COVID-19 effect in term of the number of cases anddeaths on US and Europe stock markets and results reveal that there is no impact of COVID-19 on themarket returns of US Stock market also he nds that there is a negative relationship between COVID-19cases and market returns of the Italy and France stock exchange. Nozawa, Y., & Qiu, Y. (2020) investigatethe market reaction of the corporate bonds during the COVID-19 and they nd that the Central bankpromised to support cut down the default risk for loan borrowers and further, the result shows mixedevidence about the market reactions caused by the market segments and liquidity channels.Ortmann, R., Pelster, M., & Wengerek, S. T. (2020) investigate the impact of COVID-19 outbreak on theretail investors, the ndings show that the signi cant increase has been seen in the stock trading whileincrease in the cases speci cally older age and male investors, 13.9 % trading in stock increased in theweek which affects the stock index and 9.99% decline recorded in Dow 30 on 12 th March 2020. Liu, H., Manzoor, A., Wang, C., Zhang, L., & Manzoor, Z. (2020) investigate the impact of COVID-19 onworld 21 major stock indices in short term, the results show that the word major stock markets havedirectly affected due to COVID-19 and signi cantly decreased after the COVID-19 outbreak has beenrecorded, moreover their results show that Asian countries are more affected than the other regions.Further, regression analyses reveal that there is a negative relationship between the increase in thenumber of cases and stock indices return. Çıtak, F., Bagci, B., Şahin, E. E., Hoş, S., & Sakinc, İ. (2020) investigate the effect of COVID-19 on the stockmarket they found that there is an existence of signi cant and negative impact of COVID-19 on the Stockmarket of all the countries. Heyden, K. J., & Heyden, T. (2020) investigate that what impact on the stockmarket of USA and European after the report of rst COVID-19 case in the country, the result shows thatthere is a negative relationship between the report of the rst case and the stock market, further theyidentify that the scal policy also negatively impacted on the stock market returns, improvement in thestock market has recorded after the announcement of monetary policy.
3. Research Methodology
Research Method states to technique is being used to perform research related to business; it offers atechnique to the examined outcome for particular Challenge in Research Study intended for the whole age 5/35 study is conducting, it shows the path, road-map, combination, and sense for creating dependable resultsand create outcome bene cial for every stake-holders used for that study, a proper method can Createcomprehensive outcomes or vice versa, that's why procedure retains the worth of core part in researches.In this research the secondary data has been collected of 41 major stock markets indices from the 32countries data has been collected from investing.com for stock indices from 1 st July 2019 to 14 th th May 2020. The market has been classi ed intodeveloped and the emerging market further we divided the data according to the subcontinent (MorganStanley Classi cation Index), below tables represents the classi cation of indices and countries.
Table 1 Detail of Indexes age 6/35
S.no Codes Last Country MSCI Continent1 A Dow 30 American Developed American2 B S&P 500 American Developed American3 CI Nasdaq American Developed American4 DI SmallCap 2000 American Developed American5 E S&P 500 VIX American Developed American6 G DAX Germany Developed Europe7 H FTSE 100 UK Developed Europe8 I CAC 40 France Developed Europe9 J AEX Netherland Developed Europe10 K IBEX 35 Spain Developed Europe11 L FTSE MIB Italy Developed Europe12 M SMI Switzerland Developed Europe13 N PSI 20 Portugal Developed Europe14 O BEL 20 Belgium Developed Europe15 P ATX Austria Developed Europe16 Q OMXS30 Sweden Developed Europe17 R OMXC25 Denmark Developed Europe18 S Nikkei 225 Japan Developed Pacific19 T S&P/ASX 200 Australia Developed Pacific20 U DJ New Zealand New Zealand Developed Pacific21 W STI Index Singapore Developed Pacific22 X TA 35 Israel Developed Europe1 Y Bovespa Brazil Emerging American2 Z S&P/BMV IPC Mexico Emerging American3 AA MOEX Russia Emerging Europe4 AB RTSI Russia Emerging Europe5 AC WIG20 Poland Emerging Europe6 AD Budapest SE Hungary Emerging Europe7 AE BIST 100 Turkey Emerging Europe8 AF Tadawul All Share Saudi Arab Emerging Middle East9 AG Shanghai China Emerging Asia10 AH SZSE Component China Emerging Asia11 AI China A50 China Emerging Asia12 AJ DJ Shanghai China Emerging Asia13 AK Taiwan Weighted Taiwan Emerging Asia14 AL SET Thailand Emerging Asia15 AM IDX Composite Indonesia Emerging Asia16 AN Nifty 50 India Emerging Asia17 AO BSE Sensex India Emerging Asia18 AP PSEi Composite Philippine Emerging Asia19 AQ Karachi 100 Pakistan Emerging Asia
The table above, exhibiting Indices Name, Alphabetical Codes, Country, Market, and Region Classi cation,we have categorized market and region according to the indexes of Morgan Stanley Capital International.This study had encountered 41 the best performing indexes around the globe categorized byInvesting.com, further alphabetical codes were used in the model construction.3.1 Research Procedure age 7/35 A : β returns = β = β + β %change Covid-19 case + erH B : β returns = β = β + β %change Covid-19 case + erH CI : β returns = β = β + β %change Covid-19 case + erH DI : β returns = β = β + β %change Covid-19 case + er ………………. H AQ : β returns = β = β + β %change Covid-19 case +er age 8/35 (All Regression Models from A to AQ )Model : β returns = β A = β B = β CI = β DI = β E = β G = β H =………………. = β AO = β AP = β AQ H : β returns ≠ β A = β B = β CI = β DI = β E = β G = β H =………………. = β AO = β AP = β AQ (ALL EGARCH Models from A to AQ)Model : β Indices Average returns = β
Developed Markets = β
Emerging Markets H : β Indices Average returns ≠ β Developed Markets = β
Emerging Markets (EGARCH Models on Developed and Emerging Market by Using Dummy Variables)Model : β Indices Average returns = β
America Market = β
Asia Market = β
European Market = β
Paci c and Gulf Market H : β Indices Average returns ≠ β America Market = β
Asia Market = β
European Market = β
Paci c and Gulf Market (EGARCH Models on Continental Market by Using Dummy Variables)3.1.5 Plan of Analysis / Statistical ToolsIn the rst step, we examined shifts in the global indices by using descriptive statistics from 1 st July 2019to 14 th May 2020 to compare pre and post-pandemic situation, shifts are classi ed as Median, StandardDeviation, and Relatives Ranking of each index, Median and Standard Deviation is calculated based ondaily return and compressed to the monthly returns and then this monthly median and the standarddeviation is used to assign relative rankings.In the second segment, we quanti ed the Correlation matrix before and after the pandemic to see theinternational indexes joint movement to each other, is this segment we developed two matrixes and itsanalysis.The third phase represents the impact of COVID-19 on indices return in the continent, for that weindividually run regression model by using linear regression model using the daily basis data startingfrom the rst case reported of COVID-19 in the country to 14 th of May, 2020 and see constructed acomprehensive table which illustrates how each index is effect by the % change in Covid-19 cases by itscoe cient and p-values. age 9/35 Not only we set the regression for each index, but also we see the clustering effect in each index in phasefour by using EGARCH model to use of daily basis data from 1 st July 2019 to 14 th May 2020 and see theclustering effect by dividing them into two broad categories as Developed and Emerging Market as perthe guideline of MCSI.Now in the fth phase of the research, we constructed a single index by averaging daily returns ofrespective indices in two broad categories as Developed and Emerging Market, this time we tested thejoint clustering effect on Developed and Emerging Markets by assigning it a single index throughAveraging.In the last segment phase 6 we use the same methodology as in step ve, but this time we jointly testedthe clustering effects in all of the 4 sampled continents by using the EGARCH model.We have employed Descriptive Statistics, Ordinary Least Square (OLS), Correlation Matrixes, ARCH,GARCH, TARCH, EGARCH and PARCH model, therefore we did not nd TARCH and PARCH suitable for thisstudy so we dropped these model, then from GARCH and EGRACH we selected EGARCH model accordingto the Information Criterion Tests (AIC, HIC, and HQC) and in the last of every EGARCH model, we useARCH-LM test to diagnose the ARCH type of effect in the model.OLS, GARCH, TGARCH, and EGARCH models were estimated and the model with the lowest values of AIC,SC, and HQC criterions was selected.The following EGARCH model was assessed for the study.3.1.6 Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH)The equation above denotes the constant of the variance equation; (cid:0) is the βlog + (σ2t-1) GARCH termwhich evaluates the size of the group effect in the restricted volatility of the selected indices returns. α ⌊ (Et-1)/(σt-1) ⌋ is the ARCH term which measures the closeness and scope of ARCH in uence in themeasured conditional uctuation. Is the asymmetric γ (Et-1)/(σt-1) expression which evaluates thevastness of asymmetric effect. Asymmetric term measures the size of the uneven effect in the restrictedvariations of the selected indices return. Adverse innovation, normally principals for the greatest partstimulates higher next period volatility distinguished with positive development. This component isknown as Asymmetric impact (Ding et al., 1993).
4. Results And Findings
Table 2 Descriptive Statistic for Panel 1 (America) age 10/35
Indexes Descriptive 31-Jul-19 30-Aug-19 30-Sep-19 31-Oct-19 29-Nov-19 31-Dec-19 31-Jan-20 28-Feb-20 31-Mar-20 30-Apr-20 14-May-20Dow 30 Median ofReturns 0.04% 0.18% 0.14% 0.09% 0.11% 0.11% 0.11% -0.43% -1.41% 0.17% -0.17%Stdv of Returns 0.491% 1.381% 0.578% 0.798% 0.405% 0.529% 0.788% 1.624% 6.327% 2.661% 1.577%Ranks onReturns 17 6 17 25 18 21 10 40 39 37 31Ranks on Stdv 41 10 32 27 40 38 30 18 4 8 19No. of Cases - - - - - - 6 60 164,620 1,039,909 1,390,746S&P 500 Median ofReturns 0.18% 0.07% 0.02% 0.28% 0.12% 0.09% 0.11% -0.16% -1.66% 0.58% 0.22%Stdv of Returns 0.53% 1.43% 0.56% 0.82% 0.36% 0.48% 0.75% 1.56% 5.88% 2.60% 1.55%Ranks onReturns 5 16 26 9 16 24 10 22 42 22 7Ranks on Stdv 38 7 35 24 42 40 34 23 7 11 20No. of Cases - - - - - - 6 60 164,620 1,039,909 1,390,746Nasdaq Median ofReturns 0.20% -0.11% -0.09% 0.33% 0.17% 0.20% 0.14% 0.11% -0.70% 0.77% 0.85%Stdv of Returns 0.69% 1.63% 0.85% 0.93% 0.48% 0.54% 0.87% 1.74% 5.73% 2.62% 1.69%Ranks onReturns 3 31 40 8 9 12 8 9 30 11 1Ranks on Stdv 23 3 12 16 35 37 24 13 8 10 16No. of Cases - - - - - - 6 60 164,620 1,039,909 1,390,746SmallCap2000 Median ofReturns 0.05% -0.19% -0.07% 0.13% 0.14% 0.13% -0.09% -0.24% -1.23% 1.26% -0.18%Stdv of Returns 0.74% 1.59% 1.03% 0.89% 0.70% 0.50% 0.77% 1.55% 6.79% 3.73% 2.41%Ranks onReturns 15 35 37 23 14 17 33 27 36 3 32Ranks on Stdv 19 4 8 18 16 39 31 24 3 2 2No. of Cases - - - - - - 6 60 164,620 1,039,909 1,390,746S&P 500 VIX Median ofReturns 0.08% -2.11% -2.12% -1.75% 0.63% -0.16% 0.16% 0.93% -2.24% -3.33% -2.38%Stdv of Returns 6.51% 14.41% 6.33% 7.43% 4.29% 7.45% 9.13% 15.18% 17.94% 7.28% 9.53%Ranks onReturns 13 43 43 43 2 43 3 1 43 43 43Ranks on Stdv 1 1 1 1 1 1 1 1 1 1 1No. of Cases - - - - - - 6 60 164,620 1,039,909 1,390,746Bovespa Median ofReturns 0.12% 0.43% 0.21% 0.35% -0.16% 0.33% -0.26% -0.50% -1.44% 1.37% -0.32%Stdv of Returns 0.79% 1.54% 0.66% 1.12% 0.90% 0.62% 1.35% 1.99% 7.69% 2.95% 1.52%Ranks onReturns 10 1 13 2 40 3 39 41 40 1 36Ranks on Stdv 15 5 25 3 8 27 4 8 2 4 21No. of Cases - - - - - - - 1 4,579 78,162 188,974S&P/BMVIPC Median ofReturns -0.16% 0.27% -0.09% -0.08% 0.01% -0.09% -0.07% -0.28% -1.33% 0.13% -0.13%Stdv of Returns 0.84% 1.20% 0.67% 0.85% 0.68% 0.94% 1.07% 1.13% 3.16% 1.74% 1.40%Ranks onReturns 42 2 41 37 28 42 30 31 38 38 29Ranks on Stdv 12 16 24 21 19 4 13 38 34 30 24No. of Cases - - - - - - - - 1,094 17,799 40,186 age 11/35
The purpose of keeping above mentioned statistic was to analyze the median shift in the returns of theeach index, therefore not only we reported the shift in the median as well as encountered the riskassociated with the return in term of standard deviation it has been measure, shift of median showcasedthat since Novel Covid-19 has not been declared as pandemic, the indexes above exhibiting the strongstability with steady risk associated, on 11 March Novel Covid-19 declared as pandemic by the WorldHealth Organization (WHO), then stock market aggressively shown the abnormal change and huge shiftin the average returns, we have target the indexes classi ed into American region, whereas the viruslargely hit the world biggest economy so perceive consequences in term of stock markets could be seeninto American stock market, therefore the shift of median from month to month trailing 11 monthsencountered in this research exhibited massive change each American index, Covid-19 declaredpandemic outbreak in the month of March and the massive change have been detected into eachAmerican indexes, the best ranked index become eventually the worsen such as Dow 30, S&P 500,Nasdaq, SmallCap 2000 and S&P 500 VIX ranked 39, 42, 30, 36 and 43 in Mar20 (Jul-19, 17, 5, 3, 15 and13) on basis of monthly median returns respectively, further we had ranked entire indices classi ed by theinvesting.com, hence S&P/TSX (Canada) and Hang Seng (Hong Kong) indexes were removed due toinsu cient data of Covid-19, moving forward in term of standard deviation the highest ranked indexedbest and vice versa, meaning indices with bigger rank number are consider best because risk associatedwith them are on very low level therefore comparing to this phenomena again American indexes reportedthe worsen ranked in term of risk, such as one to ve indexes shown the shift in risk 8, 11, 10, 2 and 1 inMar20 (Jul-19 41, 38, 23, 19, 1) respectively. The information also claims, when Covid-19 cases shownsigni cant increment in American ultimately put the impact on the American stock market and stood thestock market on the verge of collapse. Therefore the American stock market was classi ed as the highestvulnerable stock market around the globe. Not only there is a signi cant shift into the rank but also theaggressive shift has been observed into the American stock market within 5 months only especially in themonth of March-2020 after declaration as a pandemic. age 12/35
Table 3 Descriptive Statistic for Panel 2 (Asia)Indexes Descriptive 31-Jul-19 30-Aug-19 30-Sep-19 31-Oct-19 29-Nov-19 31-Dec-19 31-Jan-20 28-Feb-20 31-Mar-20 30-Apr-20 14-May-20Shanghai Median ofReturns 0.03% -0.11% 0.23% -0.35% -0.07% 0.24% -0.52% 0.31% -0.47% 0.25% 0.19%Stdv of Returns 0.93% 0.97% 0.75% 0.64% 0.69% 0.68% 0.94% 2.17% 1.85% 0.87% 0.51%Ranks onReturns 21 29 10 40 33 8 42 4 22 35 9Ranks on Stdv 9 33 16 35 18 24 19 5 43 43 43No. of Cases - - - - - 27 9,714 78,927 82,241 83,944 84,024SZSEComponent Median ofReturns 0.15% -0.11% 0.30% -0.31% -0.06% 0.40% -0.17% 0.58% -0.47% 0.28% 0.48%Stdv of Returns 1.26% 1.20% 1.07% 0.83% 0.98% 0.88% 1.31% 2.80% 2.42% 1.21% 0.74%Ranks onReturns 6 32 5 39 32 2 37 3 21 34 4Ranks on Stdv 3 17 6 23 4 8 5 2 40 39 37No. of Cases - - - - - 27 9,714 78,927 82,241 83,944 84,024China A50 Median ofReturns -0.09% -0.01% -0.01% 0.07% -0.12% 0.15% -0.55% -0.04% -0.49% 0.06% 0.02%Stdv of Returns 1.00% 1.09% 0.74% 0.62% 0.86% 0.70% 0.99% 2.08% 2.11% 0.87% 0.55%Ranks onReturns 36 23 32 27 38 14 43 16 23 40 20Ranks on Stdv 4 24 17 37 9 22 16 6 41 42 41No. of Cases - - - - - 27 9,714 78,927 82,241 83,944 84,024DJ Shanghai Median ofReturns -0.01% -0.11% 0.13% 0.00% -0.11% 0.24% 0.00% 0.25% -0.40% 0.30% 0.00%Stdv of Returns 0.96% 1.03% 0.77% 0.63% 0.74% 0.70% 0.92% 2.22% 1.97% 0.89% 0.52%Ranks onReturns 26 29 20 32 37 8 23 5 19 33 21Ranks on Stdv 6 28 14 36 14 21 21 4 42 41 42No. of Cases - - - - - 27 9,714 78,927 82,241 83,944 84,024TaiwanWeighted Median ofReturns -0.07% 0.04% 0.07% 0.17% 0.08% 0.13% 0.00% -0.29% -0.86% 0.46% 0.52%Stdv of Returns 0.57% 0.86% 0.45% 0.60% 0.67% 0.57% 1.40% 0.95% 3.02% 1.27% 1.21%Ranks onReturns 31 18 23 19 21 17 25 32 32 26 2Ranks on Stdv 32 38 39 40 20 33 2 41 38 38 26No. of Cases - - - - - - 9 34 306 429 440SET Median ofReturns -0.02% -0.28% 0.04% -0.08% -0.18% -0.07% -0.04% -0.42% 0.49% 0.69% 0.38%Stdv of Returns 0.48% 0.91% 0.54% 0.60% 0.69% 0.60% 1.02% 1.87% 4.51% 2.06% 1.16%Ranks onReturns 28 37 24 37 41 39 29 39 5 17 5Ranks on Stdv 42 36 37 38 17 28 14 10 21 25 28No. of Cases - - - - - - 14 40 1,651 2,954 3,017IDX Composite Median ofReturns 0.02% 0.02% -0.05% 0.19% -0.31% 0.21% -0.08% -0.34% -1.49% 0.38% -0.36%Stdv of Returns 0.54% 0.86% 0.70% 0.72% 0.62% 0.58% 0.73% 0.94% 4.01% 2.08% 0.98%Ranks onReturns 23 20 36 14 43 11 31 36 41 32 37Ranks on Stdv 36 39 21 34 26 31 35 42 25 24 35 age 13/35
No. of Cases - - - - - - - - 1,414 9,771 15,438Nifty 50 Median ofReturns -0.15% 0.17% 0.00% 0.14% 0.08% 0.08% -0.03% -0.27% -0.54% 0.76% -0.30%Stdv of Returns 0.74% 1.02% 1.63% 0.75% 0.52% 0.56% 0.77% 1.26% 4.84% 2.81% 2.21%Ranks onReturns 41 8 29 21 21 26 28 29 27 12 33Ranks on Stdv 20 30 2 33 32 35 32 33 13 6 5No. of Cases - - - - - - 1 3 1,251 33,050 78,003BSE Sensex Median ofReturns -0.09% 0.20% -0.04% 0.19% 0.07% 0.02% 0.03% -0.26% -0.50% 0.73% -0.43%Stdv of Returns 0.70% 1.01% 1.63% 0.82% 0.52% 0.57% 0.75% 1.23% 4.96% 2.90% 2.28%Ranks onReturns 36 5 35 14 26 32 19 28 24 14 39Ranks on Stdv 22 31 3 25 33 34 33 34 11 5 4No. of Cases - - - - - - 1 3 1,251 33,050 78,003PSEiComposite Median ofReturns 0.03% 0.11% -0.03% 0.19% -0.14% 0.04% -0.15% -0.05% 0.05% 0.46% -0.02%Stdv of Returns 0.93% 1.18% 0.57% 0.85% 0.95% 0.97% 1.07% 1.52% 4.84% 2.66% 1.07%Ranks onReturns 22 12 34 14 39 29 36 18 12 26 25Ranks on Stdv 8 18 34 22 5 3 12 26 14 9 31No. of Cases - - - - - - 1 3 2,084 8,212 11,618Karachi 100 Median ofReturns 0.04% -1.44% -0.01% 0.35% 0.69% 0.22% -0.13% -0.52% -0.53% 0.74% 0.14%Stdv of Returns 0.97% 1.72% 1.05% 0.95% 1.27% 1.16% 1.21% 1.28% 3.31% 2.27% 0.69%Ranks onReturns 18 42 31 2 1 10 35 42 26 13 13Ranks on Stdv 5 2 7 12 2 2 9 32 33 17 40No. of Cases - - - - - - - 2 1,625 15,759 35,788
Many articles classi ed Asian stock market as the least vulnerable market, hence this is being exhibitedby the above-mentioned table, an aggressive is seen only in Chinese stock market and rest marketreported steady risk and stable returns, going towards Chinese stock market which showcases themassive median shift in the month of February-2020 in each Chinese indexes, it is very interesting beforepandemic declaration the Chinese market more vulnerable and later proclaimed the signi cant stability interm of Monthly Average Returns, Standard Deviation, Median and Standard Deviation ranks. The numberindicates that when there is a massive change in the number of Covid-19 reported cases in January andFebruary then the market lost its stability. Coming towards rest of the stock market into Asia, Indian stockmarket shows healthy improvement in term of average monthly returns and stable rank shift, not onlyIndian Stock market but also Pakistani stock market shows stability in the global pandemic becausethese are the countries which adopted smart lockdown policy and intended to run stock market as usual. age 14/35
Table 4 Descriptive Statistic for Panel 3 (Europe)Indexes Descriptive 31-Jul-19 30-Aug-19 30-Sep-19 31-Oct-19 29-Nov-19 31-Dec-19 31-Jan-20 28-Feb-20 31-Mar-20 30-Apr-20 14-May-20DAX Median ofReturns 0.01% 0.11% 0.32% 0.34% 0.10% -0.07% -0.02% -0.03% -0.31% 0.95% -0.39%Stdv of Returns 0.81% 1.26% 0.55% 1.04% 0.51% 0.78% 1.00% 1.48% 4.66% 2.57% 1.97%Ranks onReturns 24 11 4 5 19 39 26 15 16 8 38Ranks on Stdv 13 13 36 4 34 15 15 29 19 12 9No. of Cases - - - - - - 5 47 61,913 159,119 172,239FTSE 100 Median ofReturns -0.05% -0.07% -0.02% 0.09% 0.13% 0.11% 0.01% -0.27% 0.86% 0.55% -0.05%Stdv of Returns 0.66% 1.03% 0.52% 0.91% 0.61% 0.85% 0.83% 1.48% 4.33% 2.26% 1.51%Ranks onReturns 30 27 33 25 15 21 21 29 1 23 27Ranks on Stdv 26 27 38 17 27 11 28 30 22 19 22No. of Cases - - - - - - 2 16 22,141 165,221 229,705CAC 40 Median ofReturns -0.03% 0.16% 0.23% 0.17% 0.08% 0.13% -0.02% -0.19% 0.42% 0.65% -0.75%Stdv of Returns 0.57% 1.42% 0.62% 1.02% 0.38% 0.78% 0.89% 1.59% 4.75% 2.36% 2.02%Ranks onReturns 29 9 8 19 21 17 26 23 6 19 41Ranks on Stdv 35 8 29 6 41 14 23 19 16 14 7No. of Cases - - - - - - 6 38 44,550 128,442 140,734AEX Median ofReturns 0.14% 0.13% 0.20% -0.02% 0.15% -0.01% 0.07% 0.01% 0.35% 1.23% -0.02%Stdv of Returns 0.53% 1.23% 0.44% 0.94% 0.47% 0.79% 0.92% 1.74% 4.23% 2.04% 1.90%Ranks onReturns 8 10 15 34 13 35 14 13 7 4 24Ranks on Stdv 39 15 41 14 36 13 22 14 23 26 11No. of Cases - - - - - - - 1 11,750 38,802 43,211IBEX 35 Median ofReturns -0.12% 0.21% 0.29% 0.03% -0.05% 0.02% -0.17% 0.05% -0.07% 0.40% -0.30%Stdv of Returns 0.807% 1.023% 0.629% 0.973% 0.573% 0.866% 0.822% 1.700% 4.917% 2.096% 1.656%Ranks onReturns 39 4 6 30 31 32 37 11 14 30 34Ranks on Stdv 14 29 28 9 31 9 29 17 12 23 18No. of Cases - - - - - - - 35 104,267 215,183 228,691FTSE MIB Median ofReturns 0.06% -0.22% 0.15% 0.21% 0.07% 0.14% 0.02% 0.12% 0.32% 0.95% 0.09%Stdv of Returns 0.95% 1.48% 0.69% 0.95% 0.66% 0.86% 1.18% 1.90% 5.42% 2.33% 1.81%Ranks onReturns 14 36 16 13 25 16 20 8 8 9 16Ranks on Stdv 7 6 23 13 22 10 10 9 10 15 13No. of Cases - - - - - - 3 650 101,739 203,591 222,104SMI Median ofReturns 0.09% -0.12% 0.01% 0.25% 0.19% 0.17% 0.10% 0.03% 0.58% 0.40% 0.35%Stdv of Returns 0.62% 0.97% 0.58% 0.77% 0.45% 0.68% 0.71% 1.57% 3.78% 1.42% 1.20%Ranks onReturns 12 33 28 12 8 13 13 12 3 31 6Ranks on Stdv 28 34 33 30 37 23 36 22 28 34 27 age 15/35
No. of Cases - - - - - - - 8 15,412 29,324 30,330PSI 20 Median ofReturns -0.28% 0.01% 0.22% 0.19% -0.08% 0.01% 0.04% -0.15% 0.18% 0.59% -0.77%Stdv of Returns 0.68% 1.10% 0.75% 0.60% 0.61% 0.62% 0.68% 1.49% 4.01% 1.49% 1.37%Ranks onReturns 43 21 11 14 34 34 18 21 10 21 42Ranks on Stdv 25 23 15 39 28 26 38 27 26 33 25No. of Cases - - - - - - - - 6,408 24,692 28,132BEL 20 Median ofReturns 0.14% -0.04% 0.38% 0.10% 0.17% 0.10% -0.08% -0.04% 0.21% 1.21% 0.12%Stdv of Returns 0.76% 1.25% 0.60% 1.00% 0.45% 0.58% 0.87% 2.02% 4.72% 2.47% 2.15%Ranks onReturns 7 25 3 24 11 23 31 16 9 5 14Ranks on Stdv 16 14 31 8 38 32 25 7 17 13 6No. of Cases - - - - - - - 1 11,899 47,859 53,981ATX Median ofReturns -0.15% -0.32% 0.21% 0.27% 0.08% 0.11% -0.12% -0.35% -0.61% 1.00% -0.74%Stdv of Returns 0.73% 1.00% 0.70% 0.93% 0.78% 0.59% 0.71% 1.55% 5.55% 2.75% 1.94%Ranks onReturns 40 40 12 10 21 20 34 37 28 7 40Ranks on Stdv 21 32 22 15 13 30 37 25 9 7 10No. of Cases - - - - - - - 5 9,618 15,364 15,964OMXS30 Median ofReturns -0.09% 0.00% 0.45% 0.35% -0.10% 0.06% 0.07% -0.02% 0.01% 0.43% 0.02%Stdv of Returns 0.91% 1.29% 0.71% 0.96% 0.65% 0.75% 0.95% 1.70% 3.87% 2.30% 2.29%Ranks onReturns 36 22 2 2 36 28 14 14 13 28 19Ranks on Stdv 10 12 20 11 23 18 18 16 27 16 3No. of Cases - - - - - - - 7 4,028 20,302 27,909OMXC25 Median ofReturns 0.05% 0.03% 0.03% 0.14% 0.24% 0.08% 0.22% -0.19% 0.69% 0.72% 0.17%Stdv of Returns 0.75% 1.15% 0.72% 0.96% 0.85% 0.64% 0.92% 1.58% 3.03% 1.13% 1.12%Ranks onReturns 15 19 25 21 4 27 2 23 2 15 10Ranks on Stdv 17 20 19 10 10 25 20 21 37 40 30No. of Cases - - - - - - - 1 2,577 9,008 10,667TA 35 Median ofReturns -0.08% 0.26% 0.28% -0.02% -0.01% -0.07% 0.11% 0.61% -1.10% 0.47% -0.08%Stdv of Returns 0.61% 1.12% 0.45% 0.53% 0.42% 0.37% 0.55% 1.49% 3.58% 1.84% 1.85%Ranks onReturns 35 3 7 34 29 38 10 2 34 25 28Ranks on Stdv 29 22 40 42 39 41 41 28 30 29 12No. of Cases - - - - - - - 3 4,473 15,834 16,548MOEX Median ofReturns -0.01% -0.03% -0.19% 0.19% 0.19% 0.25% 0.16% -0.29% -0.32% 0.64% -0.02%Stdv of Returns 0.66% 0.90% 0.64% 0.80% 0.64% 0.56% 0.85% 1.59% 4.03% 1.88% 0.74%Ranks onReturns 27 24 42 14 7 7 3 32 18 20 26Ranks on Stdv 27 37 26 28 25 36 26 20 24 28 38No. of Cases RTSI Median of -0.08% 0.09% 0.01% 0.45% -0.02% 0.28% 0.23% -0.56% -0.66% 1.35% 0.15% age 16/35
ReturnsStdv of Returns 0.68% 1.38% 0.87% 0.79% 0.82% 0.76% 1.31% 2.40% 5.90% 3.21% 1.74%Ranks onReturns 34 15 27 1 30 5 1 43 29 2 12Ranks on Stdv 24 11 10 29 11 16 6 3 6 3 15No. of Cases - - - - - - - 2 1,836 99,399 242,271WIG20 Median ofReturns -0.07% -0.31% 0.13% 0.05% -0.31% 0.09% 0.01% -0.36% -0.86% 0.89% -0.31%Stdv of Returns 0.57% 1.42% 0.98% 1.03% 0.91% 0.90% 1.23% 1.79% 4.71% 2.22% 1.67%Ranks onReturns 31 39 18 29 42 24 21 38 31 10 35Ranks on Stdv 34 9 9 5 7 7 8 12 18 20 17No. of Cases - - - - - - - - 2,055 12,640 17,204BudapestSE Median ofReturns 0.11% -0.30% 0.23% 0.27% 0.07% 0.27% -0.38% 0.25% -0.10% 0.71% 0.12%Stdv of Returns 0.57% 0.81% 0.74% 1.01% 0.78% 0.91% 0.98% 1.81% 4.55% 2.20% 1.00%Ranks onReturns 11 38 9 10 27 6 41 5 15 16 14Ranks on Stdv 33 40 18 7 12 5 17 11 20 21 34No. of Cases - - - - - - - - 492 2,775 3,380BIST 100 Median ofReturns 0.38% -0.09% 0.49% -0.35% 0.23% 0.29% 0.05% -0.32% -0.32% 0.49% -0.14%Stdv of Returns 1.374% 1.084% 1.122% 1.637% 0.912% 0.704% 1.385% 1.735% 3.348% 1.379% 1.069%Ranks onReturns 1 28 1 40 5 4 17 34 17 24 30Ranks on Stdv 2 25 5 2 6 20 3 15 32 35 32No. of Cases - - - - - - - - 10,827 117,589 143,114
The table above comprised on the European Stock Market, hence the Worlddometer show the daily realtime data, and the number indicates the destructions of the virus into Europe, so it is the second largestmarket after America which is succumbed of the pandemic, on today’s date (May-15-2020) Russia, Spain,United Kingdom, Italy, France, Germany and Turkey ranked in the top ten effected countries by Covid-19according to worlddometere. Median & Standard Deviation shift for these indexes are classi ed as MOEX-0.32% & 4.03% Mar-20 (Jul-19 -0.01 & 0.66), RTSI -0.66% & 5.90% Mar-20 (Jul-19 -0.08% & 0.68%), IBEX35 -0.07% & 4.91% Mar-20 (Jul-19 -0.12% & 0.80%), FTSE-100 0.86% & 4.33% Mar-20 (Jul-19 -0.05% &0.66%), FTSE MIB 0.32% & 5.42% Mar-20 (Jul-19 0.06% & 0.95%), CAC-40 0.42% & 4.75% Mar-20 (Jul-19-0.03% & 0.57%), DAX 0.31% & 4.66% Mar-20 (Jul-19 0.01% & 0.81%) and BIST-100 -0.32% & 3.34% Mar-20 (Jul-19 0.38% & 1.374%). The information concluded that aggressive increment into the Covid-19cases re ects into stock market of any country, Italy and Spain are quanti ed the highest vulnerableindexes into Europe due the worse spread of pandemic and complete lock down for many days. age 17/35
Table 5 Descriptive Statistic for Panel 4 (Pacific & Gulf) Indexes Descriptive 31-Jul-19 30-Aug-19 30-Sep-19 31-Oct-19 29-Nov-19 31-Dec-19 31-Jan-20 28-Feb-20 31-Mar-20 30-Apr-20 14-May-20Nikkei 225 Median ofReturns 0.03% 0.06% 0.20% 0.34% 0.17% -0.08% 0.07% -0.23% -1.13% -0.04% 0.06%Stdv of Returns 0.90% 0.96% 0.63% 0.76% 0.65% 0.76% 1.14% 1.47% 3.56% 2.18% 1.46%Ranks onReturns 20 17 14 5 11 41 14 26 35 42 18Ranks on Stdv 11 35 27 31 24 17 11 31 31 22 23No. of Cases - - - - - - 14 210 1,953 14,088 16,079S&P/ASX 200 Median ofReturns 0.32% 0.18% 0.13% 0.07% 0.21% -0.04% 0.13% -0.14% -1.24% 0.00% -0.02%Stdv of Returns 0.51% 1.12% 0.41% 0.81% 0.67% 0.90% 0.62% 1.18% 4.83% 1.96% 1.98%Ranks onReturns 2 6 18 27 6 36 9 20 37 41 23Ranks on Stdv 40 21 42 26 21 6 40 37 15 27 8No. of Cases - - - - - - 7 23 4,557 6,746 6,975DJ New Zealand Median ofReturns 0.18% -0.05% -0.07% -0.03% 0.30% -0.04% 0.16% -0.07% -0.50% 0.19% 0.50%Stdv of Returns 0.598% 1.042% 0.858% 0.877% 0.605% 0.595% 0.537% 1.023% 3.116% 1.573% 0.696%Ranks onReturns 4 26 37 36 3 36 3 19 25 36 3Ranks on Stdv 31 26 11 20 29 29 42 40 36 32 39No. of Cases - - - - - - - 1 647 1,129 1,147STI Index Median ofReturns -0.07% -0.36% -0.01% 0.34% -0.09% 0.04% 0.00% -0.19% -0.95% 0.09% 0.00%Stdv of Returns 0.60% 0.69% 0.60% 0.59% 0.60% 0.37% 0.64% 1.18% 3.63% 1.70% 1.05%Ranks onReturns 31 41 30 5 35 29 23 23 33 39 21Ranks on Stdv 30 43 30 41 30 42 39 36 29 31 33No. of Cases - - - - - - 13 96 844 15,641 25,346Tadawul AllShare Median ofReturns 0.13% 0.09% 0.07% -0.42% 0.17% 0.44% 0.15% -0.32% 0.15% 0.69% 0.16%Stdv of Returns 0.54% 0.76% 0.84% 0.89% 0.72% 0.72% 0.84% 0.89% 3.13% 1.34% 0.92%Ranks onReturns 9 13 22 42 10 1 7 34 11 17 11Ranks on Stdv 37 42 13 19 15 19 27 43 35 36 36No. of Cases - - - - - - - - 1,453 21,402 44,830
Panel 4 consists on Paci c and Gulf indexes, gulf region countries are least victim countries, businessactivities are impacted heavily due to coronavirus pandemic in the paci c region and the shift of above-mentioned factors support the statement, the major shift into the factors is being seen in Nikkei 225(Japan) and STI Index (Singapore) in March. Nikkei 225 reports median average return -1.13% in Mar-20(Jul-19 0.03%) and Median rank 35 in Mar-20 (Jul-19 20), however, STI Index exhibited monthly averagereturn at -0.95% in Mar-20 (Jul-19 -0.07%) with ranking as 33 in Mar-20 (Jul-19 31), in term of ranks STI isnot hit as much higher as Japan, the most considerable thing in the pandemic, according to survey Covid-19 attacks quickly on people who are above 40 years, hence a noticeable thing that Japan inherentskewed population in term of age group, there are more aged people compare to the teenage or young,further the people in an age of 30 to 35 are in established phase and they are potential investors, age 18/35 therefore it can be perceived that Japan’s market was given hit due to withdrawal of potential agedinvestors.We have taken the date before and after the pandemic, hence above mentioned correlation matrix arebefore Coronavirus cases, there are few indexes which have strongly signi cant correlation such asAmerican and European indexes, therefore the purpose was to see the relationship of the global indexesto each other, hence in the second correlation matrix, we found very different results.The European region is considered the most effective countries by Covid-19, above matrix, wasconstructed after the very rst case of Covid-19 till 14 of May 2020, therefore it is being witnessed thatbefore pandemic the market was stable and have very least correlation but after the Coronaviruspandemic entire European capital market started to travel in the same direction highlighted as in red. Asper the theory of Forbes and Rigobon (2002) the domino effect of one market to another one, if onemarket crashed in the same region then there many chances that it will affect some other market, and age 19/35 this what is seen in the correlation matrix after the very rst case of Covid-19 to the current date, thecorrelation markets indicates market to market impact in American & European regions, and these are theregions which are badly affected by the virus. able 8 Regression Model for each indexRegressionPanel-1 A B CI DI E Y Z Coefficient -0.0153 -0.015 -0.014 -0.016 0.066 -0.0492 -0.0140 t-Statistic -2.008** -2.095** -2.014** -1.828* 1.549 -3.3967 -1.5033 Adjusted R-squared 0.0361 0.040 0.036 0.028 0.017 0.1584 0.0232 Cases in Start 1 1 1 1 1 1 5 Cases in End 1,390,746 1,390,746 1,390,746 1,390,746 1,390,746 188,974 40,186 RegressionPanel-2 AG AH AI AJ AK AL AM AN AO AP AQCoefficient -0.0049 -0.0053 -0.0057 -0.0040 -0.0031 -0.0295 -0.0218 -0.0117 -0.0122 -0.0191 -0.0041t-Statistic -1.906** -1.5506 -2.129** -1.4981 -0.6476 -3.574* -3.098* -1.4801 -1.5109 -2.433** -0.8310Adjusted R-squared 0.0272 0.0147 0.0363 0.0131 -0.0072 0.1205 0.1396 0.0163 0.0175 0.0624 -0.0057Cases in Start 27 27 27 27 1 1 2 1 1 1 2Cases in End 84,024 84,024 84,024 84,024 440 3,017 15,438 78,003 78,003 11,618 35,788RegressionPanel-3 G H I J K L M N O P QCoefficient -0.009 -0.021 -0.019 -0.001 -0.015 -0.001 -0.008 -0.027 -0.013 -0.0330 -0.0206t-Statistic -1.186 -2.008** -2.183** -0.352 -2.058** -1.633* -2.318** -3.602* -1.929** -2.599*** -2.925***Adjusted R-squared 0.005 0.040 0.047 -0.016 0.044 0.022 0.072 0.187 0.037 0.0932 0.0950Cases in Start 1 2 3 1 1 3 1 2 1 2 1Cases in End 172,239 229,705 140,734 43,211 228,691 222,104 30,330 28,132 53,981 15,964 27,909RegressionPanel-3 AA AB AC AD AE R X Coefficient -0.0160 -0.0203 -0.0083 -0.0240 -0.0097 -0.0104 -0.0191 t-Statistic -1.763* -1.4585 -2.514* -2.6204 -3.990*** -1.944** -2.403** Adjusted R-squared 0.0285 0.0154 0.0945 0.1050 0.2490 0.0482 0.0761 Cases in Start 2 2 1 2 1 1 2 Cases in End 242,271 242,271 17,204 3,380 143,114 10,667 16,548 RegressionPanel-3 S T U W AF Coefficient -0.0120 -0.0417 -0.0037 -0.0322 -0.0102 t-Statistic -1.0107 -2.813*** -0.7262 -1.720* -2.063** Adjusted R-squared 0.0003 0.0824 -0.0088 0.0245 0.0590 Cases in Start 1 4 1 3 1 Cases in End 16,079 6,975 1,147 25,346 44,830 * p-value > 0.05 but < 0.10 or > 5% and < 10%** p-value > 0.01 but < 0.05 or > 1% and < 5%*** p-value < 0.01 or < 1% age 20/35
By using linear regression model, we quanti ed the relation of % change in Coronavirus cases to indexreturns, panel 1 consists on American indices, the indices from A to DI witnesses negative signi cantrelationship between % change in cases to the index returns, as much higher the percentage as much willbe a decline into the stock market, hence America is the highest affected region by Novel coronavirus andoff course due to complete lockdown in the entire states of America brought signi cant decline into thecapital market, many businesses closure and compressed of demand of basics products put the marketin trouble, supporting to above statement these are the major indices in America which have potentialrepresent entire American region collectively.Panel 2 comprised on Asian region stock indexes, due to rapid increase in Covid-19 cases in China,Thailand and Philippine impacted heavily on the economy of these countries including the stock marketas well, the outbreak took its rst breath in Wuhan (China Mainland) and travels from China to the entireworld, therefore table above showing the signi cant relationship with major indices of Chinese capitalmarket, increment in Covid-19 cases re ects into Chinese capital market, even not China is only onewhich is lack behind, Thailand and Philippine also shown the impact of percentage change into Covid-19 cases with stock market downfall.Panel 3 categorized one European based indexes, interestingly RTSI (Russia) and Budapest SE (Hungary)did not have any signi cant impact on the % change in Covid-19 cases with stock market return perhapsthe had some smart policy to deal with the global pandemic, but rest of the entire European region capitalmarkets are badly damaged by the virus pandemic that what was analyzed in the above-mentionedsegment in the correlation matrix, we have further examined the effected indexes by assigning themranks according to their Coe cients driven from linear regression model as below.
Table 9 Country wise Coefficients Indices Code Country Coefficients RanksJ Netherland -0.09% 1L Italy -0.13% 2M Switzerland -0.76% 3AC Poland -0.83% 4G Germany -0.90% 5AE Turkey -0.97% 6R Denmark -1.04% 7O Belgium -1.31% 8K Spain -1.49% 9AA Russia -1.60% 10X Israel -1.91% 11I France -1.94% 12Q Sweden -2.06% 13H UK -2.11% 14N Portugal -2.72% 15P Austria -3.30% 16
We have excluded Russian RTSI (Russia) and Budapest SE (Hungary) from the rankings because Covid-19 has no impact on these indexes, we used coe cient to account for the minor and major change in the age 21/35 % change in cases bring the unit to change into index return, therefore above mentioned ranks allow us topass comments that Austria is the highest in uential country in term of coe cient increment, suppose1% changes are detected in % change of cases so it will bring 3.30% change into the stock market returnsof ATX index, although Worldsdometer ranks Italy, Spain, Germany, and Turkey into to ten effectedcountries but above grid shows indeed a signi cant inverse relationship between % change in cases andindex return the effect is very nominal.Panel 4 is quanti ed on Paci c and Gulf-based indexes, S&P/ASX 200, STI Index and Tadawul All Shareshow a signi cant negative relationship between coronavirus cases and stock market returns, thecoe cient of the equations are very low that means there is a very minor type of effect on the indexes bythe increment in the Covid-19 cases within the country, therefore the relationship still exists and can’t beignored at all.
Table 10 EGARCH Model for each index in Developed Market classified by MSCIEGARCH Model – Each Index for Developed MarketsCode Indices MSCI Coefficient t-statistics ARCH Term Asymmetry term GARCH TermA Dow 30 Developed 0.000 0.152 0.204** -0.368*** 0.958***B S&P 500 Developed 0.001 0.001 0.283** -0.394*** 0.957***CI Nasdaq Developed 0.002 1.067 0.270** -0.316*** 0.957***DI SmallCap 2000 Developed -0.001 -0.492 0.214** -0.250*** 0.978***E S&P 500 VIX Developed -0.02 -3.151*** -0.066 0.413*** 0.878***G DAX Developed 0.006 3.814*** -0.150** -0.242*** 0.965***H FTSE 100 Developed 0.000 0.091 0.497*** -0.138* 0.948***I CAC 40 Developed -0.006 -3.529*** 0.317*** -0.151*** 0.955***J AEX Developed -0.003 -1.042 0.689*** -0.190* 0.920***K IBEX 35 Developed 0.003 4.433*** -0.151*** -0.283*** 0.959***L FTSE MIB Developed 0.006 6.165*** -0.248*** -0.369*** 0.937***M SMI Developed 0.005 4.109*** 1.0317*** -0.478*** 0.119*N PSI 20 Developed -0.003 -1.818* 0.728*** -0.10619 0.884***O BEL 20 Developed 0.002 1.257 0.158*** -0.277*** 0.959***P ATX Developed -0.002 -0.600 0.156** -0.185*** 0.984***Q OMXS30 Developed 0.006 3.609*** -0.150** -0.231*** 0.962***R OMXC25 Developed 0.002 1.358 0.232*** -0.222*** 0.897***S Nikkei 225 Developed -0.000 -0.39 0.066 -0.216*** 0.974***T S&P/ASX 200 Developed -0.003 -2.606** 0.381*** -0.081 0.957***U DJ New Zealand Developed 0.001 0.605 0.169** -0.127*** 0.965***W STI Index Developed -0.000 -0.352 0.202** -0.190*** 0.958***X TA 35 Developed -0.001 -0.824 0.369*** -0.123** 0.975***p-value > 0.05 but < 0.10 or > 5% and < 10%** p-value > 0.01 but < 0.05 or > 1% and < 5%*** p-value < 0.01 or < 1%
Not only we detected the effect and intensity of the Covid-19 on stock indices but also we haveencountered clustering effects into each index classi ed as Developed and Emerging markets, tomeasure volatility in the stock indices we have used ordinary least square (OLS), GARCH, TARCH, PARCHand EGARCH models, as per the information criterions (AIC, HIC, and HQC) the least valuable one modelis the best, we have selected to used EGARCH model in both of the panels, we have tested every single age 22/35 index to nd the clustering effect in the markets by using dummy in place of reported cases on dailybasis, below is the list of cumulative cases on the last terminal day of this research.
Table 11 Cumulative Cases in Developed Markets with respect to CountryS.no ISO3 Codes Country Market Cumulative Cases % of Cumulative Cases1 USA American Developed 1,390,746 52.27%2 GBR UK Developed 229,705 8.63%3 ESP Spain Developed 228,691 8.60%4 ITA Italy Developed 222,104 8.35%5 DEU Germany Developed 172,239 6.47%6 FRA France Developed 140,734 5.29%7 BEL Belgium Developed 53,981 2.03%8 NLD Netherland Developed 43,211 1.62%9 CHE Switzerland Developed 30,330 1.14%10 PRT Portugal Developed 28,132 1.06%11 SWE Sweden Developed 27,909 1.05%12 SGP Singapore Developed 25,346 0.95%13 ISR Israel Developed 16,548 0.62%14 JPN Japan Developed 16,079 0.60%15 AUT Austria Developed 15,964 0.60%16 DNK Denmark Developed 10,667 0.40%17 AUS Australia Developed 6,975 0.26%18 NZL New Zealand Developed 1,147 0.04%Total Total 2,660,508 100.00%
It has discussed previously, America found the highest affect country around the globe; around 1.3mcases are reported in US and New Zealand as the least cases around the world. Anyhow, according toabove mentioned EGARCH equation witnessed the smart policies of Portugal and Australia because thePSI 20 & S&P/ASX 200 asymmetry term is insigni cant, which indicates that in both of the indexes thereis no instable uctuation which harms market decorum, therefore it is also noticeable that these bothmarket are saved from markets shocks generated by the bad news. Coming towards the rest of theindices, almost every index reported the clustering effects because p-value of the EGARCH Term is underaccepted regions, meaning we cannot reject the alternative hypothesis, there are clustering effect in themodel, meaning period of low volatility is followed by period of low volatility for prolonged period andperiod of high volatility is followed by period of high volatility, if one day return is negative then therepossibilities that next will be negative too and this pattern remains same with a certain time, and whathas been seen in the rest of the indexes in developed market. It is also noticeable that most of the indexescategorized into the developed market are from America or Europe and that is the red zone area of Covid-19, since it is declared a global pandemic entire markets shows negatives returns mentioned by themodel above.
Table 12 EGARCH Model for each index in Emerging Market classified by MSCI age 23/35
EGARCH Model – Each Index for Developed MarketsCode Indices MSCI Coefficient t-statistics ARCH Term Asymmetry term GARCH TermY Bovespa Emerging -0.002 -0.756 0.254*** -0.255*** 0.945***Z S&P/BMV IPC Emerging 0.001 2.410*** -0.142*** -0.250*** 0.958***AA MOEX Emerging 0.000 0.146 0.201*** -0.128*** 0.967***AB RTSI Emerging 0.000 0.227 0.179 -0.140*** 0.971***AC WIG20 Emerging 0.003 2.368** -0.122*** -0.250*** 0.962***AD Budapest SE Emerging -0.001 -0.277 0.178** -0.127*** 0.970***AE BIST 100 Emerging 0.001 0.938 0.000 -0.239*** 0.930***AF Tadawul All Share Emerging 0.007 3.824*** 0.072 -0.265*** 0.896***AG Shanghai Emerging -0.001 -1.551 1.076*** -0.136* -0.158AH SZSE Component Emerging 0.003 1.976** 0.111 -0.226*** 0.889***AI China A50 Emerging 0.000 0.948 0.037 -0.264*** 0.906***AJ DJ Shanghai Emerging -0.002 -1.339 0.797 -0.149 -0.205AK Taiwan Weighted Emerging 0.005 3.324 0.034 -0.207*** 0.935***AL SET Emerging 0.002 2.921** -0.146*** -0.298*** 0.961***AM IDX Composite Emerging -0.001 -0.443 0.107 -0.197*** 0.965***AN Nifty 50 Emerging 0.002 2.168** 0.041 -0.226*** 0.980***AO BSE Sensex Emerging 0.002 2.144** 0.022 -0.233 0.98AP PSEi Composite Emerging -0.012 -9.487*** 1.138*** 0.057 -0.203***AQ Karachi 100 Emerging 0.000 0.277 0.168** -0.106** 0.942**** p-value > 0.05 but < 0.10 or > 5% and < 10%** p-value > 0.01 but < 0.05 or > 1% and < 5%*** p-value < 0.01 or < 1%
In the emerging market, most of the countries are from Asian and Paci c regions, apart from the Chinacross border countries to China are declared red zone in the pandemic. In above table Fourth and Fifthcolumn is the mean equation in the EGARCH model and rest of the columns for the EGARCH, result fromthe emerging market are far different from the developed market such as the biggest index of china andIndia DJ Shanghai and BSE Sensex had no clustering effect on pandemic even PSEi Composite does nothave any clustering effect because p-value of Asymmetry term (EGARCH Model) more than 0.10% whichmeans negative news or shock does not impact the market equilibrium, therefore smart policies andinitiative to retain the capital taken by the Indian, Chinese and Philippine looks fruitful stabilized themarket equilibrium. EGARCH refers to two broad criteria such as it measures the volatility into the stockmarket as well as the role of information meaning negative and positive shocks into the market, beforemoving forward we mentioned the table which indicated cumulative cases in Emerging Markets.
Table 13 Cumulative Cases in Emerging Markets with respect to Country age 24/35
S.no ISO3 Codes Country Market Cumulative Cases % of Cumulative Cases1 RUS Russia Emerging 242,271 26.67%2 BRA Brazil Emerging 188,974 20.81%3 TUR Turkey Emerging 143,114 15.76%4 CHN China Emerging 84,024 9.25%5 IND India Emerging 78,003 8.59%6 SAU Saudi Arab Emerging 44,830 4.94%7 MEX Mexico Emerging 40,186 4.42%8 PAK Pakistan Emerging 35,788 3.94%9 POL Poland Emerging 17,204 1.89%10 IDN Indonesia Emerging 15,438 1.70%11 PHL Philippine Emerging 11,618 1.28%12 HUN Hungary Emerging 3,380 0.37%13 THA Thailand Emerging 3,017 0.33%14 TAI Taiwan Emerging 440 0.05%Total Total 908,287 100.00%
Russia, Brazil, and Turkey show the highest reported cases the in the above-mentioned table, whereasChine remains on the number, therefore in the indexes of Russia, Brazil and Turkey strong clusteringeffect has been detected by EGARCH model, whereas in rest of the Chinese indices show aggressiveclustering effect apart from Shanghai index, we have dummy variable in the palace of every reportedcase to developed EGARCH equation, hence almost every index individually effect by the Covid-19reported by the regression model above and there are few indexes which have Covid-19 impact but notclustering effect. Further, in the above-mentioned table, the emerging market indicates that there is aclustering effect in each index apart from the Shanghai, BSE Sensex, and PSEi Composite.
Table 14 Market wise Classification of EGARCH ModelDeveloped – Emerging MarketEGARCH MODEL Developed Market Emerging Market Coeff. z-stat. Prob. Coeff. z-stat. Prob.Mean Equation Mean Equation C 0.00032 1.025625 0.3051 C 0.000154 0.350885 0.7257Developed Market 0.002213 3.31E+00 0.0009 Emerging Markets 0.001389 2.14E+00 0.0321AR (1) 0.177919 2.669502 0.0082 AR (1) 0.187777 2.822439 0.0052Variance Equation Variance Equation C -0.26665 -3.06681 0.002 C -0.82169 -3.43228 0.0006ARCH Term -0.03495 -0.6348 0.526 ARCH Term 0.393685 4.363591 0.0000Asymmetry term -0.26024 -7.62018 0.000 Asymmetry term -0.14025 -2.61873 0.0088GARCH Term 0.972621 138.7587 0.000 GARCH Term 0.948585 42.9176 0.0000ARCH LM Test ARCH LM Test t-Statistic Prob. t-Statistic Prob. 0.108679 0.9136 -0.23796 0.8121
In every individual phase of this research paper, we found European market is the most affected marketby the Covid-19 and most of the indexes are in the developed market comes under European region,therefore we needed to hypothesize the Covid-19 clustering effect on Developed and Emerging Marketcollectively, before this segment we analysis the market on an individual basis by using regression and age 25/35
EGARCH Model, but in this segment, we have averaged out the daily return of each index and plugged thisinto the respective category and created single indexes for developed and emerging markets.Initially, we tested the ARCH effect in the both constructed equation and found ARCH effect because p-value of AR(1) is less than 0.05 or 5% which allow us to use ARCH family model, therefore we useOrdinary Least Square, GARCH, TARCH, EGARCH and PARCH model, we selected EGARCH modelaccording to least value of AIC, SIC, and HQC.Interesting both of the Developed and Emerging Markets exhibited positive returns in these 11 months onthe average basis shown by the mean driven equation the rst part of the model, invariance equation, ithas been witnessed that developed and emerging markets have strong clustering effect because p-valueof Asymmetry term is negative and less than 0.05 or 5% which further claims that the negative shockwithin the market effect more rather than the positive shocks, which means huge increment in cumulativecases make signi cant persistent volatility for long period, further coe cients of the mean and varianceequation are higher in the Developed Market compare to the Emerging Market, meaning DevelopedMarket is more volatile and have persistent clustering effect. Finally, we use diagnostic as ARCH-LM testwhich indicates that equations do not have the ARCH type of effect; therefore p-value of ARCH-LM test inboth markets is more than 0.05 or 5% which proclaims that both equations do not have the ARCH type ofeffect.
Table 15 Continent wise Classification of EGARCH Model age 26/35
Continent ClassificationEGARCH MODEL American Market Asian Market Coeff. z-stat. Prob. Coeff. z-stat. Prob.Mean Equation Mean Equation C 0.00095 0.480074 0.6312 C -0.000181 -0.405055 0.6854American Market 0.0000438 1.91E-02 0.9847 Asian Market -0.004955 -1.92E+00 0.0549AR (1) 0.303847 4.70879 0.000 AR (1) 0.201858 3.043132 0.0026Variance Equation Variance Equation C -8.618946 -0.54581 0.585 C -0.521984 -3.676844 0.0002ARCH Term 0.01 0.182267 0.855 ARCH Term 0.200308 3.192093 0.0014Asymmetry term 0.01 0.357303 0.721 Asymmetry term -0.150435 -4.340833 0.000GARCH Term 0.01 0.00551 0.996 GARCH Term 0.961391 78.37841 0.000ARCH LM Test ARCH LM Test t-Statistic Prob. t-Statistic Prob. 4.604761 0.000 -0.938532 0.349 European Market Pacific & Gulf Market Coeff. z-stat. Prob. Coeff. z-stat. Prob.Mean Equation Mean Equation C 0.000279 0.522072 0.6016 C -0.0000134 -0.031752 0.9747Europeon Market -0.097015 -9.49E-01 0.3428 Pacific & Gulf Market 0.000786 8.76E-01 0.3809AR (1) 0.268005 4.098165 0.0001 AR (1) 0.390356 6.260474 0.000Variance Equation Variance Equation C -0.35313 -3.264434 0.001 C -0.464996 -3.427611 0.0006ARCH Term 0.142783 2.415525 0.016 ARCH Term 0.138435 1.815372 0.0695Asymmetry term -0.2156 -6.277141 0.000 Asymmetry term -0.2078 -5.46053 0.000GARCH Term 0.973524 99.29631 0.000 GARCH Term 0.964017 84.55819 0.000ARCH LM Test ARCH LM Test t-Statistic Prob. t-Statistic Prob. 0.096569 0.9232 1.335432 0.1831
As per the Morgan Stanley Capital International region classi cation guideline, we have further dividedour model continent wise; the above table has the segments, (1) Mean Equation and (2) VarianceEquation. We can understand the Covid-19 cases globally by the below-mentioned table and pie chart. age 27/35
The table above shows the cumulative cases and percentage of the Covid-19 cases on the last terminalday of this research as of May 15, 2020. Europe stood on rank 1 by having around 45.57% cases in thecontinental ranking, while testing the Covid-19 Effect in correlation and regression model we found theheavy intervention of pandemic to shock the European markets, but in this segment, we have to test thevolatility or clustering effect in each of the continents, we have averaged out the returns of all indiceswhich come under European region and us single index as European index then we regressed this on thecumulative cases in the European region, in mean equation AR(1) show p-value less than 0.05 or 5%which allow us to use the ARCH family model, as per the Information criterion AIC, SIC, and HQC weselected EGARCH model is the most suitable model. p-value of Asymmetry term in European MarketModel is less than 0.05 or 5% with a negative coe cient, which supports the negative shocks impactmore rather than the positive news, Covid-19 is one of the kinds of a negative shock to the stock marketand the value of the coe cient of European Market Model the highest amongst all of them for models,that further indicates the presence of stock market anomalies with market clustering in European indices,moreover below is the bar chart of countries in the European region with cumulative percentages Covid-19 Cases on last terminal day of this research.By using the same methodology as above, we have averaged out all index and constructed the singleindex as Paci c and Gulf, hence again AR(1) do not limit us to use ARCH family model, therefore by usingEGARCH Model we have driven mean and variance equation and came to the ndings by de ning theclustering effect into the region, in the last segment we employed diagnostic test as ARCH-LM test andthe p-value is more than 0.05 or 5% which means there is no ARCH type of effect in the model and modelis t to predict results. The further below-mentioned graph illustrates country name along with % ofcumulative Covid-19 cases in the European region.See Figure 3 uploaded in gure section.By using same methodology as above, in paci c region, which is considered the second least affectedcontinent by the Covid-19 but it has the second-highest clustering effect, because the countries under thisregion started lockdown earlier then Asia and the region has more reported Covid-19 cases compare toAsia, which became the cause of clustering effect into the Paci c and Gulf region, however marketexhibits the instability in term of daily volatility witnessed by the negative coe cient and p-value (< 0.05or < 5%) of Asymmetry term, initially ARCH test did not limit us to use ARCH family models, hence weemployed GARCH, TARCH, EGARCH and PARCH model, and found EGARCH as the most suitable model tomeasure Covid-19 clustering effects into Paci c and Gulf Market which further proclaims that negativenews or shocks impact a lot than the positive news of shocks to the stock market, therefore rapidincrement in Covid-19 on daily basis created signi cant negative news/shocks and that is noticeable signfor the clustering effect into the Paci c and Gulf region. In the last segment, we employed a diagnostictest as ARCH-LM test and the p-value is more than 0.05 or 5% which means there is no ARCH type ofeffect in the model and the model is t to predict results. Further, the graph below exhibits the comprisedcountries and % cumulative Covid-19 cases on the last terminal day of this research. age 28/35
See Figure 4 uploaded in gure section.To drive result for Asian Stock market we use the same pattern of calculating index and regressed it forEGARCH model as above, initially we applied the ARCH test to see whether long term volatility is followedby another period of long term volatility or in easy words can ARCH family models be used to witnessclustering effect into the region, hence the test allowed us to use ARCH family models, we found EGARCHmodel is the best-suited model, Asian markets reported the negative abnormal returns, meaning as Covid-19 cases increases in the market produce the negative downwards, going forward it has also beennoticed that market has strong clustering effect in the pandemic period. According to worlddometer andrest of the other authentic sources, Asia is the least affected region by Covid-19 (Apart from China), twofactors became the cause of cushion or savior for Market from the virus effect, (1) Smart LockDown inthe Market and (2) Rapid Recoveries from the Virus, further, we can’t ignore the role of cumulative casesin Asia which still lesser than rest of the other regions, and we found the least volatility into the market.We employed the ARCH-LM test to check model tness and the p-value is less than more than 0.05 or 5%which further indicates there is no ARCH type of effect in the model and model is t to use for prediction.Below is the graph which illustrates % of Covid-19 cases in the pandemic.See Figure 5 uploaded in gure section.America is considered second the highest affected region by Covid-19, interestingly we could not ndclustering effect into American stock market, by using the same methodology as above we employed theEGARCH model and found indeed there is a signi cant relationship between individual American Indexand Covid-19 cases, but there is no clustering effect as illustrated by the value of the Asymmetry termmarket is not effect by the negative shock or news in the pandemic, in the light of the above numbers wecan claim that America uses good Standard Operating Procedure (SOPs) to stabilize the capital market inAmerica. ARCH-LM test de nes the model tness because p-value is more than 0.05 or 5%, further belowis the graph of % of Covid-19 cases in the American Countries.See Figure 6 uploaded in gure section.We have also constructed model with continent indexed daily average returns with cumulative cases inthe continent and also with dummy variable by replacing cumulative cases into EGARCH model,employed data is divided into categories (1) before the Covid-19 cases not a single case reported in theregion (we use 0 as dummy variable) and (2) After the very rst cases Covid-19 (we use 1 as dummyvariable). We found the rst model not suitable because nding were against logics so we dropped thatmodel and decided to select the second model with the dummy variable and the model is with the logicand realities.
5. Discussions And Conclusion age 29/35 umbrella approach to see market movement before and after pandemic by using different models, in the rst phase of the research we used descriptive statistics by classifying it region-wise as per themethodology of MSCI, in which we see the median shift, standard deviation shift and relative rank shiftsbefore and after the pandemic, plus also try to relate the cumulative cases with these shifts into eachsampled index. In the second phase, we have seen the correlation matrix where is used to see the jointmovement of entire sampled indexes to each other before and after the pandemic, in the third phase wetested linear regression model for each index by categorizing them region-wise as per the MCSIclassi cation to see the impact of % change into Covid-19 with single individual index, not only theimpact of Covid-19 to index return we also encountered the clustering effect-Volatility in each index byusing EGARCH model in this is classi ed as Developed and Emerging Market as per the methodology ofMSCI this is in phase four. With the same method by dividing markets into two broad segments asDeveloped and Emerging Market, we collectively tested clustering effect by using EGARCH model inphase ve, in the last phase six we classi ed the indexes into four broad categories as America, Asia,Europe, and Paci c & Gulf by assigning them a single index and constructed variance equation by usingEGARCH model.5.2 Conclusion: as per international reports, articles and other authentic source, region Europe found thehighest affected region around the globe, hence our research found the European Market in muchvulnerable and risk, shifts in descriptive statics shown the trend how European market goes from better togood and worse nally, in these 11 months shifts indicates not only returns are affected by the Covid-19in European capital market but also ranking with peer group became worsen, further the correlationmatrix illustrated after the rapid increase in the Novel Coronavirus Cases entire European marketexhibited highly correlated market, means almost all big indexes in Europe move closely to each otherand the linear regression model supported the this movement by illustrating the signi cant impact on theMarket Returns, moreover in MSCI index classi cation most indexes in Developed Market are fromEuropean countries, due to this Developed ARCH model reported clustering effect in the developedmarkets, and nally in the last model we have found European Market has the highest volatility in relationto Covid-19.With the rapid increase of Covid-19 cases in America affected American indexes, we found that Americanindexes exhibited signi cant shifts in term of average monthly returns, standard deviation, and relativerankings before and after the announcement of the pandemic by World Health Organization, we foundAmerican Indices as stable because of intellectual and smart investment policies for capital markets,indeed there is a signi cant and strong impact of Covid-19 on indices returns but no evidence found forclustering effect/volatility in American indices.Paci c & Gulf region countries reported the third-highest Covid-19 cases, therefore we found the greatshift in medians, standard deviation and relatives ranking in Paci c & Gulf indices, further we alsodetected Covid-19 impacts on indexes daily returns, therefore in the last segment we found the Paci c &Gulf indexes are classi ed as the second highest volatile market having strong clustering effect incontrast to Covid-19 cases. age 30/35
Due to fewer cases reported in the Asian region and smart lockdown policy, we found that Asian stockindex indeed has Covid-19 impact on indexes but least clustering effect compare to Europe and Paci c &Gulf, median shift indicated that after the declaration of pandemic (11-March-2020) market reported asigni cant change in each index on Asian capital Market, therefore we can conclude Asian Capital Markethas the third effect market by the Covid-19.
Declarations
It has been declared that there is no con ict of interest any of the authors
References
Alfaro, Laura, Anusha Chari, Andrew N Greenland, and Peter K Schott. 2020. Aggregate and rm-levelstock returns during pandemics, in real-time. National Bureau of Economic Research.Baker, Scott, Nicholas Bloom, Steven J Davis, Kyle Kost, Marco Sammon, Tasaneeya %J CovidEconomics: Vetted Viratyosin, and Real-Time Papers. 2020. "The unprecedented stock market reaction toCOVID-19." 1 (3).Barro, Robert J, José F Ursúa, and Joanna Weng. 2020. The coronavirus and the great in uenzapandemic: Lessons from the “spanish u” for the coronavirus’s potential effects on mortality andeconomic activity. National Bureau of Economic Research.Bartik, Alexander W, Marianne Bertrand, Zoë B Cullen, Edward L Glaeser, Michael Luca, and Christopher TStanton. 2020. How are small businesses adjusting to COVID-19? Early evidence from a survey. NationalBureau of Economic Research.Cajner, Tomaz, Leland D Crane, Ryan A Decker, Adrian Hamins-Puertolas, and Christopher Kurz. 2020."Tracking labor market developments during the covid-19 pandemic: A preliminary assessment."Çıtak, Ferhat, Bugra Bagci, Eyyüp Ensari Şahin, Safa Hoş, and İlker %J Available at SSRN 3596931Sakinc. 2020. "Review of stock markets' reaction to COVID-19 news: fresh evidence from Quantile-on-Quantile regression approach."Global change data lab. (2020). Our World in Data. Retrieved 20 May, 2020, fromhttps://ourworldindata.org/coronavirus-testingGormsen, Niels Joachim, and Ralph SJ %J University of Chicago Koijen, Becker Friedman Institute forEconomics Working Paper. 2020. "Coronavirus: Impact on stock prices and growth expectations." (2020-22).Heyden, Kim J, and Thomas %J Available at SSRN 3587497 Heyden. 2020. "Market Reactions to theArrival and Containment of COVID-19: An Event Study." age 31/35
Figures age 32/35
Figure 1WHO Coronavirus Disease (COVID-19) Dashboard. Note: The Graph has taken from the WHO o cialwebsite on 16th May 2020, the graph consists of the number of new cases, the aggregate number ofcases, and the number of death on the left-hand side, size of the circle showing the number of cases inthe region or country. Note: The designations employed and the presentation of the material on this mapdo not imply the expression of any opinion whatsoever on the part of Research Square concerning thelegal status of any country, territory, city or area or of its authorities, or concerning the delimitation of itsfrontiers or boundaries. This map has been provided by the authors. age 33/35
Figure 2Case Comparison WHO Regions. Note: The graph is taken from the o cial website of WHO on 16th May2020, which represented the number of cases according to the subcontinent, the graph indicating thatmost cases have been reported in the America and Europe subcontinent, further if classi ed the marketmost of the developed market belongs to America and Europe Countries.Figure 3European region age 34/35
Figure 4Paci c and Gulf regionFigure 5Asia age 35/35age 35/35