Assessing the Nexus Between Stock Market Performance and Covid-19: Evidence from Global Perspective

12 This study described an empirical link between COVID-19 fear and stock market volatility. Studying 13 COVID-19 fear with stock market volatility is crucial for planning adequate portfolio diversification in 14 international financial markets. The study used AR (1) – GARCH (1,1) to measure stock market volatility 15 associated with the COVID-19 pandemic. Our findings suggest that COVID-19 fear is the ultimate cause 16 driving public attention and is a stock market volatility. The results demonstrate that stock market 17 performance and GDP growth decreased significantly through average increases during the pandemic. 18 Further, a 1% increase in COVID-19 cases the stock return and GDP decrease with a 0.8%, 0.56%, 19 respectively. However, GDP growth demonstrated a slight movement with stock exchange. Moreover, 20 public attention to the attitude of buying or selling was highly dependent on the COVID-19 pandemic 21 reported cases index, death index, and global fear index. Consequently, investment in the gold market, 22 rather than in the stock market, is recommended. The study also suggests policy implications for key 23 stakeholders.


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The global economy was hit hard by the COVID-19 pandemic, which created panic in global 27 financial markets. The aim of this study was two-fold: first, to determine how much of the increase 28 in stock volatility can be traced to COVID-19, and second, to examine the relevant economic 29 factors, such as financial development and economic growth. Drastic and impactful changes have 30 been witnessed since the beginning of 2020, and their impact has led to various events that have 31 interfered with different aspects of human life, including the social and economic arenas. Due to 32 this impact, several economies are currently trying to recover from recessions. The pandemic hit 33 almost every aspect of the economy hard, including consumption, trade, manufacturing, supply 34 chains, and financial behaviours. Due to COVID-19 uncertainty, massive recovery plans are called 35 for worldwide to counter these adverse effects on economies. Different approaches have been 36 suggested by different sources internationally, but the ideal recovery method is considered to be 37 based on sustainable post-COVID-19 strategies (Yoshino et al., 2020). Therefore, countries should    (Rowan & Galanakis, 2020;Daughton, 2020). 80 The purpose of this study was to test the positive and negative impacts of COVID-19 on stock 81 market volatility. Our contribution also included the assessment of COVID-19 and stock price 82 comments. We used the AR (1) -GARCH (1, 1) model, which is reliable for estimating the impact 83 of COVID-19 fear on public attention to stock market volatility. This study also aimed to evaluate 84 the plans and measures taken by different developed nations, the reasons for such approaches, and 85 the efficiency of their recovery strategies (W. . We contributed to this area in 86 several ways. We investigated the relationship between COVID-19 and stock exchanges, such as 87 S&P500, NASDAQ, DOW, DAX, CRIA, and Cyprus. We highlighted that economic recovery 88 strategies have been called for while the pandemic is ongoing.

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The rest of the paper is organised as follows: Section 2 summarises the literature review and 90 background. Section 3 explains the techniques used in this study. Section 4 describes the results 91 and discussion, and Section 5 concludes the study and provides policy implications. Monetary Fund statistics, as a world leader in automobile exports, the Japanese economy was 101 likely to shrink by 8% during the 2020 third quarter and later expected to expand to the new 102 normal. Despite Japan's strategies, the country's economy did not respond as quickly as expected.

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The overall recovery plan is sub-divided into two phases: economic emergency support. During 104 this period, the government would attempt to stabilise the economy as much as possible until the 105 end of the contagion (N. . It is a strategic period immediately following the end 106 of the pandemic, the timing of which is unknown. This is a period when demand will be stimulated 107 to normal levels, and other monetary tools will be in full control. During this period, there will be 108 a focus on the most affected sectors, such as tourism, the leisure industry, and service sectors 109 (restaurants, bars, etc.). Some of the strategies involve direct stipends to all citizens and to selected 110 businesses to cover the minimum daily needs and to compensate a part of their business loss. The 111 stipend also aims to maintain the daily demand for goods at a normal level. This will not only 112 maintain purchasing power but also boost industrial output. Support plans also include the 113 provision of loans at almost 0% interest. The country identified the core importance of the money 114 supply within the economy and decided to provide risk-and interest-free loans through the Bank 115 of Japan (Ashraf, 2020a), with the aim of encouraging more business people and other stakeholders 116 to expand their business scope and retain the labour force .

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A total amount of JPY 1 billion was set aside to support small-scale businesses. To ensure 118 the continuous flow of goods into Japan, Japanese customs declared import duty relief on all 119 imported goods. This facilitates the availability of goods and provides price relief to consumers, 120 thus boosting demand to a more normal level. This plan was accompanied by a sophisticated 121 subsidy budget for goods that are less in demand during an era of lockdowns. It has been well 122 explained that Japan cannot rely on external demand to recover its economy, but it could try to 123 reduce the COVID-19 impact through massive export of automobiles, optimal operation of their 124 heavy industries, and further investment in high-tech goods that are still in high demand, despite 125 the pandemic. This will indirectly improve the Japanese economy's progression towards with study topicality is still a missing link. Therefore, this study hypothesised a significant 134 relationship between the COVID-19 fear index and public attention to stock market volatility.

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The world economy has faced enormous setbacks owing to the intensive lockdowns that  They also demonstrated that the word 'corona' has sharp, complex, and recent similarities. Zhang Using these equations, the co-movement of the variables was established for operationalisation, 197 and these results were robust with the GARCH method. With the complete disruption of the 198 economy, government should focus more on expansionary fiscal policies to stimulate recovery.

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This mainly means to increase government spending, which will increase cash flow and liquidity 200 of assets, thereby putting more money in the hands of citizens to encourage higher demand for 201 goods and services. This, in turn, will increase the levels or volumes of supply in the economy.

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Wavelet coherence is an empirical framework used to examine the relationship between 203 two or two variables. To justify the use of wavelet coherence, we used two series sequences called 204 x (t) and y (t), which justify and clarify the function as

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Wherever the given wavelet transforms of x(t) and y(t) contribute in the objective function, for 207 example, Wx (m, n) and Wy (m, n), the wavelet index to evaluate as n; therefore, the composite 208 conjugate solution as the sign *.
Consequently, the wavelet transform is used as an empirical method to measure the non-stationary hard threshold reduction objective function can be written as follows: 225 Mid thresholding is used as a flexible technique, which ensures that the mid-level reduction in the 226 objective function is as follows: The cross-wavelet transforms ℎ and can be described in the form of W.X.Y. = W.X.W.Y. * , 231 whereas W.X. and W.Y. characterise bi-transforms designed for wavelets X and Y, respectively.

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The mixed debate ( ) can be described as the original equivalent stage:

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The level of assurance associated with the likelihood p and for density likelihood purposes is Uϑ 235 (p), explained through the square root of multiplication of two χ2 distributions. The Granger 236 causality (GC) incidence field is described to categorise the two series based on supernatural 237 interdependency. Breitung and Candelon (2006) demonstrated that the aforementioned test shows 238 the association between x and y in the VAR (p) mathematical model, as follows: The null hypothesis was verified through the test proposed by Geweke (1992) 1).

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The study results were stationary at l(1). The economy can recover mainly through geographical 262 subsidies to encourage firms to invest in depressed areas, such as the agricultural sector. This 263 means that a return shock in one economy will only have a short-term impact on other markets.

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This shows the pandemic had a significant influence on the returns of major stock exchange stock 267 markets. We can see that SP500 revenue overflow was the largest (6.089%), SP500's DAX

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In the short term, Figure 1 shows another island in the top right corner, where the co-movement 365 between these two variables is higher at the end of the wave. The small island in the left centre of 366 Figure 1 shows that the reported cases are countercyclical associations with stock performance.

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Economic growth is higher with higher trade openness and education rates and lower due to higher  matter of the data, and all the study constructs were found to fit at the first difference (see Table   385 4). The AR (1) -GJR (1, 1) model provided certain fascinating insights. The intercept (β0) and  The relationship between stock market volatility and the COVID-19 pandemic has directly 393 affected stock returns. The results in Table 4 show that almost all stock exchange returns were 394 negatively associated with the number of COVID-19 cases during this period; for example, a 1%  Considering the study results, we found novel findings that S&P500, Cyprus, CRIA, DOW, 417 Shanghai, and NASDAQ correlated significantly with the global fear index of COVID-19. The 418 coefficients of the study were significant at a p-value of 5%. Surprisingly, the Shanghai Stock 419 Exchange and DAA had a larger coefficient. The volatility coefficients of all sample international 420 stock exchanges were also statistically significant at the 5% level (table 5)

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C0 is the constant factor, C1 is the COVID-19 fear index, Υ is the dependent variable of study, β is the 433 coefficient of the variance equation for the study stock market volatility index, υ is the level of autonomy 434 parameter that equates to 2 for every market, λ is the asymmetry or skewness parameters, Γ is the AR (1) 435 estimation parameter, Λ and Ω are the GJR (1, 1) estimation parameters. 436 The β coefficient of the variance equation for the study stock market volatility index was 437 also significant at the 5% level (see table 6). The proposed outcomes recommended for 438 marketplace volatility index variance for all international stock markets, with the exclusion of the 439 S&P500, were deeply co-moved by the global fear index (GFI) of the COVID-19 outbreak. The 440 autonomy parameter υ equates to 2 for every market except the S&P500 index. A 'sharp-V' 441 recovery feature also characterises the German economy. This means that the economy fell 442 sharply, and it regained its form instantly. This shape economically implies a rapid loss and regain 443 in employment pattern, gross domestic product, and industrial output rate. Finally, the economy, 444 that is why it has adopted heavy penalties on citizens who fail to abide by face mask laws (50 445 euros). Therefore, from the strategies mentioned above for the country, it has been outlined that 446 the Dutch economy will first shrink by 11% and will later expand rapidly and become more robust 447 than before. Compared with the overall return spillover results, Table 7 shows that the long-term 448 frequency of volatility frequency is 0.7429, which has the largest total volatility spillover rate, 449 followed by mid-term volatility (0.049%) and short-term volatility (0.297%), indicating the impact 450 of each market transaction on volatility spillover. More importantly, the NASDAQ (28.433%) 451 increased the most volatility spilled over to day trading during the peak period of COVID-19 cases, 452 followed by S&P500 (7.384%), DAX (4.574%), and DOW (4.435%). This finding indicates that 453 the volatility spillover effect between COVID-19 cases and stock market returns was greater than 454 the income spillover effect. At the same time, the S&P500 had the largest volatility spillover from 455 DAX (24.556%), DOW had the largest volatility impact from S&P500 (38.195%), and Cyprus had 456 the largest volatility impact from S&P500 (46.901%). 457 Table 7. GARCHX estimation for robustness for GFI and SMVI co-movement  fear on public attention to stock market volatility and discussed the environmental effects.

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Although the March 2020 COVID-19 stock crash was considered a significant decrease in the 508 stock market, the stock exchange market showed a 26% reduction over four days. The massive 509 reduction in stock exchanges caused the US GDP to decrease by 4.8% during the first quarter of 510 2020 and the unemployment rate to decrease by 20%.

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Our results showed that the increase in confirmed cases and deaths caused by the coronavirus 512 was related to a significant decline in market liquidity and stability. Then, the market rebounded,  Another essential part is the micro-, small-, and medium-sized enterprises (MSMEs), which 536 shape over 50% of the economies in many countries. Supporting MSMEs through credit guarantee 537 schemes, low-interest loan programs, and tax cuts, especially during the COVID-19 pandemic, 538 could have multiple advantages for recovering from the economic recession, creating jobs, and 539 decreasing imports. Considering the challenge of controlling the pandemic, how and when the 540 COVID-19 crisis will end will determine the parameters of different policy responses.

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Ethical Approval and Consent to Participate: The authors declare that they have no known 542 competing financial interests or personal relationships that seem to affect the work reported in this 543 article. We declare that we have no human participants, human data or human tissues.