Linkages Between Brent Oil Price And Iran Stock Market: New Evidence From The Corona Pandemic
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Abstract This article reviews the relationship between the oil market and the stock market during the Corona outbreak. This study aims to analyze the stock market and the effect of oil prices on this market during the corona pandemic. The hypothesis of this paper is whether while oil prices shocks happen due to business cycle fluctuations and some other reasons like political reasons, occur; The correlations between changes in Brent oil prices and stock market indices tend to be affected by named corona indexes. Forecasting the stock market in each period has been difficult and the value of stock index has been affected by various factors. Among these factors has been the oil and gas sector, especially in countries dependent on the revenue from their sales. On the other hand, the outbreak of Covid-19 pandemic has led to profound changes in both areas. This study examines relationship between Brent oil price and Iran stock market Index during the outbreak of corona pandemic. Research method is, vector autoregression model (VAR) which using daily data covering the period from February 20, 2020 to August 21,2020. The findings of this study suggest that a negative causal effect from Brent oil price changes to the Iran stock market Index. Also, the results of impulse response functions and variance decompositions showed that some corona pandemic indicators have significant effects on the stock index.JEL Classification: I18, E44, Q4, C5
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Linkages Between Brent Oil Price And Iran Stock Market: New Evidence From The Corona Pandemic | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Linkages Between Brent Oil Price And Iran Stock Market: New Evidence From The Corona Pandemic Vida Varahrami, Masoumeh Dadgar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-409534/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This article reviews the relationship between the oil market and the stock market during the Corona outbreak. This study aims to analyze the stock market and the effect of oil prices on this market during the corona pandemic. The hypothesis of this paper is whether while oil prices shocks happen due to business cycle fluctuations and some other reasons like political reasons, occur; The correlations between changes in Brent oil prices and stock market indices tend to be affected by named corona indexes. Forecasting the stock market in each period has been difficult and the value of stock index has been affected by various factors. Among these factors has been the oil and gas sector, especially in countries dependent on the revenue from their sales. On the other hand, the outbreak of Covid-19 pandemic has led to profound changes in both areas. This study examines relationship between Brent oil price and Iran stock market Index during the outbreak of corona pandemic. Research method is, vector autoregression model (VAR) which using daily data covering the period from February 20, 2020 to August 21,2020. The findings of this study suggest that a negative causal effect from Brent oil price changes to the Iran stock market Index. Also, the results of impulse response functions and variance decompositions showed that some corona pandemic indicators have significant effects on the stock index. JEL Classification : I18, E44, Q4, C5 Econometrics Other Economics Corona virus oil price stock market index VAR Covid-19 Brent oil price Tehran stock Index Gold price Media Hype Index fake news Index Media Coverage Index and Panic Introduction In late 2019 and early 2020, the world faced a new wave of crisis called the Corona Pandemic. The global pandemic of the Corona pandemic is one of the greatest social and economic crises in human history. It is predicted that with the outbreak of this pandemic, global GDP will decrease by 4.6% in 2020 [1]. Furthermore the COVID-19 pandemic has made over 4.3 million confirmed cases. This has raised concerns about an impending economic crisis and recession. Social distance, personal isolation and travel restrictions have reduced work in all sectors of the economics [2]. While there is limited previous literature on how pandemics affect financial markets, its effects can be seen in parallel with the effects of other forms of natural disasters. These studies would seem to suggest that the that the effect of pandemic outbreaks on different economic sectors of countries has been through different channels. One obvious way that pandemics like the corona can affect financial systems is their huge economic costs [3]. Governments use a variety of financing methods to meet these costs. The Corona pandemic has also affected businesses and organizations, affecting financial markets and the global economy in a chain reaction. Meanwhile, unusual reactions from governments have led to disruptions in the supply chain [4]. Under these circumstances, oil-dependent countries are facing financing problem and Economic uncertainty has increased in these countries; Therefore, in these crises, the stock market can be considered as one of the financing centers for the government. Due to the outbreak of COVID-19 [3] in the Middle East and North Africa, oil prices fell sharply. As a result, trade around the world locked in and restrictions on transportation led to negative oil demand [5]. In previous studies, the relationship between the stock market and oil prices has been extensively analyzed. In different time periods, the study of these two sections will be important and different crises have had different effects on this relationship in different countries. Among these crises have been crises such as the outbreak of corona heart disease. Recent studies have shown that during the 22 trading days (February 24 to March 24), 18 stock market jumps were recorded, 16 to 18 of which were in response to "bad news" about corona pandemic. Therefore, corona pandemic is a source of systematic risk and there is a need for further research on the financial effects of corona pandemic development [2]. This article deals specifically with the relationship between oil prices and the Iranian stock market due to the prevalence of the corona pandemic. In addition, the role of stock markets in developing economies has become more important since the global crises, and their economies are becoming more involved in the international oil market [6]. The last decade has seen dramatic changes in crude oil prices. One of the most important is the sharp drop in prices in 2008. Resumption of the upward trend after the financial crisis due to oversupply and declining global demand for energy goods. [7]. Ferreira and et al [8] found that stock markets are now more exposed to oil price fluctuation than before the 2008 financial crisis. To review the relationship between oil prices and the stock market index with the outbreak of the corona pandemic, this article uses data from February 19, 2020 to August 21, 2020. The data are Brent oil prices, the Iranian stock index and some indicators related to the corona pandemic news and the method used in this research is vector autoregression regression. The rest of the paper is arranged as follows. Section 2 reviews related works with most focus on the research for COVID-19 and relationship between stock market and oil price. Section 3 discusses in details about methodology and used data. Section 4 describes the results of some tests and impulse response functions and variance decompositions. Section 5 evolves this work with a brief of key findings. [3] Coronavirus disease 2019 Literature Review Oil price shocks and the reaction of monetary policy by the oil producer have been an important topic of theory in modern literature. Jammazi et al [9] showed significant bidirectional causal relations between oil and stock markets at the different time horizons for France, Germany, Italy, Spain, the UK and the US. Xu and et al [10] showed that strong evidence of asymmetries in volatility shocks between the oil and stock markets due to bad volatility. Also, Bahmani et al. [11] studied asymmetric causality not only from oil price to stock returns but also from stock returns to oil price. They found that an increase in oil price causes returns of three sectors of the U.S. economy, while a decrease in oil price causes returns of four sectors of the U.S. economy, all in the short run. Delpachitra et al [12] examined the economic outcomes of oil price shocks and supply of oil while allowing for interaction between domestic and foreign monetary policy. They concluded that domestic monetary policy is a important channel that computes for over 40% of discounted variation in domestic output across a 4-year horizon after an oil shock. In contrast, US monetary policy is less important in transmitting oil price shocks to the oil-exporting economy through the international channel. Also, Köse and Ünal [13] studied the impact of oil price shocks on the stock exchanges of three countries in the Caspian Basin − Iran, Kazakhstan and Russia. The results showed, in these three countries, the impact of negative oil price shocks on the stock market was greater than the positive shocks. The response of the stock exchanges in the three countries to negative oil shocks was highly significant. Bakas and Triantafyllou [14] showed the impact of economic uncertainty about global pandemics on the volatility of the broad commodity price index also on the sub-indexes of crude oil and gold. The conclusion of their study showed that uncertainty related to pandemics have a negative impact on the volatility of commodity markets and especially on crude oil market, while the effect on gold market is positive but less significant. Mokni [15] studied the dynamic reaction of a set of oil-related countries’ stock markets to oil price shocks. He found that the stock returns react more to supply shocks than supply shocks. In addition, the impact of supply shocks on stock returns is generally limited and negative, while aggregate demand shocks have a positive effect on almost all stock returns. Oil demand shocks have positive effects on oil exporter stock returns and negative effects on oil-importing countries, except the Chinese market. Engelhardt et al [16] showed while the covid-19 was driven by news attention or rational expectations about the pandemic’s economic impact. Using a sample of 64 national stock markets, which account for 94% of the world's gross domestic product, they present that the fall in stock markets is largely accompanied by more attention to news and less than reasonable expectations. Basher et al [17] concluded the effect that oil market shocks have on stock prices in the fall of the oil exporter is for both domestic and international investors. They studied the nonlinear relationship of oil price shocks with stock market returns in major oil-exporting countries in a multi-factor Markov-switching framework. A portfolio that uses the possibility of Markov switching to move between low-volatility stocks and volatile T-banknotes works better than a buy-and-hold strategy for some countries. Salisu and et al [18] found the impact of own and cross oil price and stock prices shocks during the post-announcement of COVID-19 to be more pronounced for oil and stocks albeit with a larger impact for the former. Azimli [19] investigated the impact of the corona pandemic on the degree and structure of risk-return dependence in the US. Following the COVID-19 outbreak, degree of dependence among returns and market portfolio have increased in the higher quantiles. Lyócsa and Molnár [20] use a nonlinear autoregressive model to show that abnormal Google searches related to COVID-19. Al-Awadhi et al [21] studied whether contagious infectious diseases have effect on stock market outcomes. They examined the effect of the COVID-19 virus by using panel data analysis on the Chinese stock market. They concluded that both the daily growth in total confirmed cases and in total cases of death caused by COVID-19 have strong negative effects on stock returns across all companies. He et al. [22] showed the impact of the corona pandemic on the stock prices of some Chinese industries. They concluded that the pandemic negatively impacted stock prices on the Shanghai Stock Exchange, whiles it positively effected the stock prices on the Shenzhen Stock Exchange. Ashraf [23] examined the stock markets’ response to the COVID-19 pandemic. He showed that stock markets response more proactively to the growth in number of COVID-19 confirmed cases as compared to the growth in number of deaths. He also shows negative market response was strong during early days of confirmed cases. He also finds that stock markets quickly react to COVID-19 pandemic and this reaction is different over time depending on the phase of outbreak. Liu et al. [24] examined the short-term effects of the corona pandemics on 21 major stock indices. Using the study event method, they concluded that these indices fell rapidly after the corona outbreak. Indices of Asian countries experienced lower negative returns compared to other countries. Topcua and Gulalb [25] studied the impact of COVID-19 on emerging stock markets. The findings display that the negative impact of pandemic on emerging stock markets has piecemeal fallen and then begun to pull in. The outbreak effects the highest in Asian emerging markets whereas emerging markets in Europe have experienced the lowest. Khantavit [26] performed the stock market response test to COVID-19 using the event study method. The results of this study showed that the stock returns of the world, France, Germany, Italy, Spain, the United States, China, the Philippines and Thailand to the Covid-19 pandemic have been significant and negative. In these countries, reactions to the widespread media coverage of the COVID-19 have been greater than the events and situations taking place. In other words, the markets' reaction to the old news was greater than the new news. Hanke et al. [27] using the risk-neutral densities of six world-famous stock indexes to assess the stock market's preparation for economic shocks, concluded that financial markets had failed to mitigate the major economic effects of COVID-19 predict until late February. This behavior in the market lasts until about mid-March, but from mid-March onwards, market behavior changes. They found that stock markets in countries with lower mortality (Japan, Germany, and the US) are more optimistically than those with higher mortality (France, Italy). Ali et al. [28] investigated the impact of COVID-19 on different financial securities and compared the situation of China and other countries but paid less attention to industry heterogeneity. Qin et al. [29] investigated the impact of the pandemic on oil markets. Liu et al. [30] studied the impact of COVID-19 on crude oil prices and stock prices in the US. According to the above literature, the prevalence of corona pandemic has undoubtedly overshadowed the relationships of economic variables. Therefore, it seems necessary to study its effects on various economic relations. This study is the first comprehensive study on the effects of the corona pandemic on the relationship between oil prices and the stock market in Iran. Using the daily statistics of the variables used, the results of the VAR model estimation are examined in the next section. Data And Methodology The main purpose of this article is to investigate the effect of the corona pandemic on the relationship between oil prices and the total Iranian stock index. The sample period of February 20, 2020 to August 21,2020. The reasons for choosing this period are, firstly, the existence of daily required data in statistical centers, secondly, the existence of oil price shock in this period and thirdly, the outbreak of corona pandemic in the sample selected period. Also, daily data show the relationship between the independent variable and the dependent variable due to the high frequency. Daily data have also been used in the studies of Cepoi [31], Kocaarslan and Soytas [32] and Mensi et al. [33] who have done a topic related to the subject of the present study. Model variables with the following symbols and definitions are included in the model: Table 1- Variables introduction Variable Statement Resource Tepix Tehran stock Index www.tse.ir BOP Brent oil price www.eia.gov Covid Total Corona confirmed cases www.behdasht.gov.ir Gold World gold price www.federalreserve.gov MHI [4] This Index showes the percentage of news talking about the novel coronavirus. Values range between 0 and 100 coronavirus.ravenpack.com Panic [5] This Index showes the level of news chatter that makes reference to panic or hysteria and coronavirus. Values range between 0 and 100. coronavirus.ravenpack.com Fake_news This Index showes the level of media chatter about the novel virus that makes reference to misinformation or fake news alongside COVID-19. Values range between 0 and 100 coronavirus.ravenpack.com Mediaco [6] This Index showes the percentage of all news sources covering the topic of the novel coronavirus. Values range between 0 and 100 coronavirus.ravenpack.com Sentim [7] This Index showes the level of sentiment across all entities mentioned in the news alongside the coronavirus. The index ranges between -100 and 100 coronavirus.ravenpack.com Info [8] This Index shows the percentage of all entities (places, companies, etc.) that are somehow linked to COVID-19. Values range between 0 and 100 coronavirus.ravenpack.com In this paper, Vector Autoregressive (VAR) model is used to analyze the relationship between variables. Vector autoregression model is one of the successful and flexible models in multivariate time series analysis. In this model, the effect of unexpected shocks is also investigated. This effect is usually determined by examining the impulse response functions and analysis of variance. To estimate the model to achieve the results, it is necessary to go through several steps in all research. There are some tests to examine the model. The following sections include these tests. Empirical Results To examine the stationary of the variables, some unit root tests, augmented dickey fuller, Phillips-perron and breakpoint were used. The results of the unit root test of variables are shown in Table 2. Table 2 Unit root test variable ADF test BP test PP test 1st difference level 1st difference level 1st difference level BOP -13.04838 -2.77307 -14.19636 -4.898668 -13.0571 -2.836142 covid -14.0783 -2.702396 -14.77559 -4.208126 -14.04418 -2.76927 Tepix -10.08292 -1.614724 -11.387 -2.708662 -10.85715 -1.614724 MHI -8.057414 -7.692997 -18.49894 -8.564565 -21.23531 -7.692997 gold -12.74439 -1.278322 -14.0478 -3.127227 -12.79557 -1.604293 fake_news -11.46549 -9.640716 -18.58898 -10.98816 -41.22403 -9.544499 mediaco -10.83647 -12.85037 -33.56785 -923.601 -166.3668 -12.85037 panic -9.371086 -10.44197 -20.76069 -17.25826 -58.60437 -10.46091 sentim -6.324472 -3.422174 -14.75188 -4.401719 -14.27376 -3.569898 info -9.282874 -7.925341 -20.31271 -8.799036 -24.353 -7.925341 Source: Researcher findings Table 3 - Final results of unit root test variable ADF test BP test PP test 1st difference level 1st difference level 1st difference level BOP stationary nonstationary stationary nonstationary stationary nonstationary covid stationary nonstationary stationary nonstationary stationary nonstationary Tepix stationary nonstationary stationary nonstationary stationary nonstationary MHI stationary stationary stationary stationary stationary stationary gold stationary nonstationary stationary nonstationary stationary nonstationary fake_news stationary stationary stationary stationary stationary stationary mediaco stationary stationary stationary stationary stationary stationary panic stationary stationary stationary stationary stationary stationary sentim stationary nonstationary stationary stationary stationary nonstationary info stationary nonstationary stationary stationary stationary stationary Source: Researcher findings In this paper, the optimal lag is determined by Akaike information criterion (AIC) Final prediction error (FPR) and Hannan-Quinn (HQ). According to the table below, the optimal lag for the model is lag one. Table 4 VAR Lag Order Selection Criteria Lag LogL LR FPE AIC SC HQ 0 -8994.284 NA 1.79E+36 111.8545 112.0458 111.9322 1 -7717.172 2379.711 8.01e+29* 97.23195* 99.33726* 98.08679* 2 -7632.562 147.1490* 9.82E+29 97.42312 101.4423 99.05509 3 -7559.779 117.5374 1.42E+30 97.76123 103.6944 100.1703 4 -7484.862 111.6772 2.08E+30 98.07282 105.9199 101.259 5 -7415.353 94.98047 3.39E+30 98.4516 108.2126 102.4149 6 -7351.376 79.47513 6.30E+30 98.89908 110.574 103.6396 7 -7240.669 123.7719 7.09E+30 98.76607 112.3549 104.2837 8 -7134.162 105.8457 9.36E+30 98.68524 114.188 104.98 Source: Researcher findings Cointegration test is to observe the long-term equilibrium relationship between the non-stationary variables. In this study, cointegration tests were accomplished out using the Johansen’s cointegration method. Table 5 shows that there are there are at least 3 long run relationships. The trace statistic value proves it and the maximum eigenvalue that greater than the critical value. Table 5- Johansen cointegration test result Hypothesized No. of CE(s) Trace Statistic 0.05 Critical Value Prob None * 450.2339 239.2354 0 At most 1 * 329.4797 197.3709 0 At most 2 * 233.4116 159.5297 0 At most 3 * 150.0963 125.6154 0.0007 At most 4 93.14551 95.75366 0.0746 At most 5 57.98253 69.81889 0.3025 At most 6 32.22011 47.85613 0.6 At most 7 13.86497 29.79707 0.8481 At most 8 5.254209 15.49471 0.7812 At most 9 1.545217 3.841466 0.2138 Source: Researcher findings Table 6 Variance Decomposition of Tepix Period S.E. Tepix sentim panic mediaco MHI info gold fake_news covid bop 1 26676.16 100 0 0 0 0 0 0 0 0 0 2 37950.51 99.42507 0.006277 0.032212 0.045005 0.124134 0.07671 0.00433 0.274387 0.010544 0.001332 3 46780.19 98.91242 0.008818 0.069344 0.033678 0.297816 0.219076 0.014669 0.401205 0.037272 0.005702 4 54354.4 98.45556 0.010914 0.104309 0.025074 0.477893 0.365123 0.028743 0.441882 0.077093 0.013413 5 61131.43 98.04455 0.01402 0.132165 0.022456 0.643872 0.498391 0.044725 0.448544 0.126725 0.024553 30 166146.4 93.86177 0.483191 0.117418 0.072221 1.122567 1.160545 0.404458 0.28934 1.480923 1.007572 31 169498.7 93.77598 0.499799 0.113831 0.072071 1.109439 1.159249 0.414137 0.286324 1.511819 1.057347 32 172824.6 93.69319 0.515611 0.110375 0.071871 1.096435 1.157698 0.423366 0.283424 1.541124 1.106902 33 176125.5 93.61329 0.530641 0.10705 0.071626 1.08359 1.15593 0.432148 0.280635 1.568913 1.156172 34 179402.8 93.53619 0.544905 0.103852 0.071343 1.07093 1.153983 0.440487 0.277951 1.59526 1.205097 35 182658 93.46179 0.558422 0.100777 0.071027 1.058478 1.151886 0.448387 0.275367 1.620238 1.253623 36 185892.3 93.39001 0.571216 0.097822 0.070683 1.04625 1.149667 0.455856 0.27288 1.643914 1.301701 37 189106.7 93.32076 0.583309 0.094983 0.070314 1.034257 1.147348 0.4629 0.270485 1.666357 1.349287 38 192302.4 93.25395 0.594729 0.092256 0.069925 1.022511 1.14495 0.46953 0.268179 1.68763 1.396342 39 195480.5 93.1895 0.605501 0.089637 0.069518 1.011016 1.142489 0.475754 0.265958 1.707794 1.442832 40 198641.7 93.12734 0.615653 0.087121 0.069096 0.999777 1.139982 0.481583 0.263819 1.726908 1.488726 Source: Researcher findings Table7 - Variance Decomposition of bop Period S.E. Tepix sentim panic mediaco MHI info gold fake_news covid bop 1 4.763361 0.05428 0.347567 0.006679 0.203992 0.037375 1.707587 0.41721 0.852942 0.022763 96.34961 2 6.340502 0.039132 0.185809 0.93393 0.132232 0.71332 1.233928 0.246961 0.850452 0.036076 95.62816 3 7.287228 0.027223 0.135238 1.636722 0.187122 1.490961 0.859076 0.172015 0.97162 0.060817 94.45921 4 7.913059 0.032147 0.139379 2.189311 0.286014 2.271526 0.677511 0.171024 0.983939 0.094716 93.15443 5 8.349105 0.055375 0.183526 2.620996 0.395368 2.995302 0.611356 0.229758 0.951017 0.136136 91.82117 30 9.944225 4.717895 4.084736 3.595267 1.219549 6.502887 1.168386 7.489288 0.469627 1.841331 68.91103 31 9.950775 5.006479 4.184458 3.562898 1.220056 6.479821 1.172189 7.752679 0.464028 1.896786 68.26061 32 9.956583 5.298516 4.277519 3.531102 1.219921 6.455267 1.17543 8.004356 0.458763 1.950036 67.62909 33 9.961729 5.593633 4.36416 3.499965 1.219212 6.429499 1.178169 8.24416 0.453802 2.001089 67.01631 34 9.966286 5.891479 4.444631 3.46955 1.217994 6.402756 1.180459 8.472016 0.449121 2.049966 66.42203 35 9.970319 6.191731 4.519191 3.439902 1.216323 6.375241 1.182349 8.687929 0.444693 2.096697 65.84594 36 9.973887 6.494085 4.588106 3.411052 1.214252 6.347131 1.183881 8.891973 0.440498 2.141317 65.2877 37 9.977041 6.79826 4.651641 3.383016 1.211827 6.318577 1.185096 9.084281 0.436514 2.183871 64.74692 38 9.979828 7.103997 4.710062 3.3558 1.209088 6.289709 1.186026 9.265041 0.432724 2.224407 64.22315 39 9.982291 7.411055 4.763631 3.329403 1.206074 6.260639 1.186703 9.434482 0.42911 2.262979 63.71592 40 9.984466 7.719214 4.812605 3.303814 1.202819 6.231461 1.187155 9.592876 0.425658 2.299643 63.22476 The results of variance decompositions show that most of the changes in the variables after the shocks on them are explained by those variables themselves. Also, the change of the total stock index variable after the shock is first explained by the variable itself, then by total corona confirmed cases, oil price, Coronavirus Infodemic index, Coronavirus Media Hype Index, Coronavirus sentiment index, gold price, Coronavirus Fake_news, the Panic Index and finally Coronavirus Media Coverage Index, respectively. As it is clear from the numbers in the table above, the number of daily patients has the most explanation. [4] Coronavirus Media Hype Index [5] Coronavirus Panic Index [6] Coronavirus Media Coverage Index [7] Coronavirus Sentiment Index [8] Coronavirus Infodemic Index Discussion The main propose of this paper is to understand the effects of Corona pandemic on the relationship between oil price and Iran stock index by some indicators, which are introduced and prepared for each country in raven pack data base and this paper has used Iran’s indicators about Corona pandemic. Finding inverse and direct relationship between stock index and the indicators of Corona pandemic, In terms of oil prices in regression, account for effect Corona pandemic in the stock market and in general the feelings of investors in stock market of Iran. These analyzes supply strong elements to figure out how, after a health crisis as corona pandemic, it has been feasible to achieve the relationship between this crisis and the stock market of Iran. Conclusion The current paper investigates examines the relationship between oil prices and the Tehran stock index with the presence of the Corona pandemic. To examine the impact of Corona, the indicators have been used. These indicators include the number of total Corona confirmed cases, fake news about the corona, Coronavirus Media Coverage Index, the panic index, Coronavirus Media Hype Index, the sentiment index and the infodemic index. Descriptions related to each of these indicators are provided in the data introduction section. While oil prices shocks happen due to business cycle fluctuations and some other reasons like political reasons, occur; The correlations between changes in Brent oil prices and stock market indices tend to be affected by named corona indexes. Using the data of the period from February 20, 2020 to August 21,2020 and using the vector autoregression model, we came to the conclusion that first of all the variables used in the article except the variables Media Hype Index, fake_news Index, Media Coverage Index and Panic with once differentiation is sustained, the variables mentioned above are also sustained. The results of the optimal lag also suggested the optimal 1 lag. Using an optimal lag, the Johansson test also proved four significant long-run relationships between the variables. This finding showed that the dynamics of stock markets during the outbreak of the coronavirus cannot be accidental. Capelle and Desroziers [34] research has shown that it is not the pre-crisis state of economies that causes current stock market reaction to the corona pandemic, but the adoption of some major government policies that have led to the dynamism of stock markets. He cited government health policies against the Corona pandemic and government support for companies affiliated with health products and services as examples. The results of the model indicate that the unprecedented rise in the stock market cannot be justified in the short term with the outbreak of the Corona virus. In this study, the Corona Media Index, which due to the significant negative short-term effect it has on the value of the stock exchange, indicates the impact of the stock market on the news in the days when the Corona news was high. With the increase in the number of social and digital networks, it is possible that this index and similar indicators will have a much higher impact on the value of the stock exchange transaction. Also, over time, this connection will become more logical and transparent. The empirical results of Impulse response functions showed that the response of the stock index to the shock on the Infodemic Index and Media Hype Index is negative. Also, the response of the total index to the total Corona confirmed cases was positive. Against the Fake news Index, it tends to be positive in the short run and to a neutral value in the long run. Regarding the effect of the shock on oil prices and its effect on the total stock index, it can be said that there was a negative relationship from the beginning to the end of the period and a shock on oil prices reduces the stock index. We also found a positive correlation between the oil price response to the shock on the Sentiment Index, Tepix, the Media Coverage Index and the total Corona confirmed cases. And with a positive shock on these indicators, a positive effect on oil prices was observed. A positive shock on the Panic Index, Media Hype Index, the Infodemic Index and gold also had a negative effect on oil prices. Finally, fake news has a positive effect in the short run and a negative effect in the long run, and Media Coverage Index shows a positive effect in the short run and a neutral effect on oil prices in the long run. This finding implies a mutual risk transmission between oil and stock markets because of the financialization of the crude oil market and the unison movement of oil and stock markets over the last few decades mainly driven by changes in global aggregate demand. The results also show that the causal interactions tend to be stronger at the coarser time scales and are particularly pronounced during periods of economic and financial turmoil such as the recent global financial crisis and European sovereign debt crisis. Declarations Ethics approval and consent to participate The authors approve that they consent to participate. Consent for publication The authors are satisfied with the publication. Availability of data and materials Access to all references mentioned in the text Competing interests The authors declare that they have no competing interests. Funding Not applicable Authors' contributions Authors are equally involved in writing the article. Acknowledgements Not applicable Authors' information Vida Varahrami, economics department, shahid Beheshti university, Tehran, Iran Masoumeh Dadgar, economic and social science faculty, Alzahra university. Tehran, Iran References Ru, Hong ; Yang, Endong ; Zou, Kunru ;. (2020). 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(N.D.). The COVID-19 Outbreak And Affected Countries Stock Markets Response. International Journal Of Environmental Research And Public Health, 17 (8). Doi:Doi.Org/10.3390/Ijerph17082800 Cepoi, Cosmin-Octavian ;. (2020, October). Asymmetric Dependence Between Stock Market Returns And News During COVID-19 Financial Turmoil. Finance Research Letters, 36:101658 . Doi:Https://Doi.Org/10.1016/J.Frl.2020.101658 Kocaarslan, Baris ; Soytas, Ugur ;. (2019, November ). Asymmetric Pass-Through Between Oil Prices And The Stock Prices Of Clean Energy Firms: New Evidence From A Nonlinear Analysis. Energy Reports, 5 , 117-125. Doi:Https://Doi.Org/10.1016/J.Egyr.2019.01.002 Mensi, Walid ; Hkiri, Besma ; Al-Yahyaee, Khamis H. ; Kang, Sang Hoon ;. (2017, March). Analyzing Time–Frequency Co-Movements Across Gold And Oil Prices With BRICS Stock Markets: A Var Based On Wavelet Approach. International Review Of Economics And Finance, 54 , 74-102. Doi:10.1016/J.Iref.2017.07.032 Capelle-Blancard, Gunther ; Desroziers, Adrien ;. (2020). The Stock Market And The Economy: Insights From The COVID-19 Crisis. Social Science Research Network . Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-409534","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research","associatedPublications":[],"authors":[{"id":24478396,"identity":"d6c73fef-6d6d-4939-b460-0149b60453ef","order_by":0,"name":"Vida Varahrami","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIie3RsQrCMBCA4QtCp5auFqF9hStO4su0OHQUNwWHlELcHMVHcbQc1CUPoNRBF3cRhCxiFBRcUt0c8kNy3PBBIAA22x+G6+d43Cw/MA7v9StS4K8EnPaLGAu4G1/G0/3QX5CYqBWBP1szGptJN5DVqbesU1F7kqAtEyilkTiJPoTwIEwQwBagND3QB2eg+I0w0mSkNImaSasKckGImoCnCTYRR5NePieM67ToeDJzY5nyBsKKHb8ShnV2PKtVPww3RGcTgejwubv6T43AZrPZbF90B7D7VKzC70TVAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-1869-8852","institution":"Shahid Beheshti University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Vida","middleName":"","lastName":"Varahrami","suffix":""},{"id":24478397,"identity":"f8e730af-e814-49ff-9d9c-9f6a89a1692b","order_by":1,"name":"Masoumeh Dadgar","email":"","orcid":"","institution":"Alzahra University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Masoumeh","middleName":"","lastName":"Dadgar","suffix":""}],"badges":[],"createdAt":"2021-04-10 20:21:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-409534/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-409534/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":13691490,"identity":"89262f1b-c8d9-4997-a8a3-e06bd132cf8d","added_by":"auto","created_at":"2021-09-17 12:38:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":285386,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-409534/v1/8d5f57ae-6bf5-41f1-99ff-fffff215f971.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eLinkages Between Brent Oil Price And Iran Stock Market: New Evidence From The Corona Pandemic\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn late 2019 and early 2020, the world faced a new wave of crisis called the Corona Pandemic. The global pandemic of the Corona pandemic is one of the greatest social and economic crises in human history. It is predicted that with the outbreak of this pandemic, global GDP will decrease by 4.6% in 2020 [1]. Furthermore the COVID-19 pandemic has made over 4.3 million confirmed cases. This has raised concerns about an impending economic crisis and recession. Social distance, personal isolation and travel restrictions have reduced work in all sectors of the economics [2].\u003c/p\u003e\n\u003cp\u003eWhile there is limited previous literature on how pandemics affect financial markets, its effects can be seen in parallel with the effects of other forms of natural disasters. These studies would seem to suggest that the that the effect of pandemic outbreaks on different economic sectors of countries has been through different channels. One obvious way that pandemics like the corona can affect financial systems is their huge economic costs [3]. Governments use a variety of financing methods to meet these costs. The Corona pandemic has also affected businesses and organizations, affecting financial markets and the global economy in a chain reaction. Meanwhile, unusual reactions from governments have led to disruptions in the supply chain [4]. Under these circumstances, oil-dependent countries are facing financing problem and Economic uncertainty has increased in these countries; Therefore, in these crises, the stock market can be considered as one of the financing centers for the government. \u0026nbsp;Due to the outbreak of COVID-19\u003ca href=\"#_ftn1\" name=\"_ftnref1\"\u003e[3]\u003c/a\u003e in the Middle East and North Africa, oil prices fell sharply. As a result, trade around the world locked in and restrictions on transportation led to negative oil demand [5].\u003c/p\u003e\n\u003cp\u003eIn previous studies, the relationship between the stock market and oil prices has been extensively analyzed. In different time periods, the study of these two sections will be important and different crises have had different effects on this relationship in different countries. Among these crises have been crises such as the outbreak of corona heart disease. Recent studies have shown that during the 22 trading days (February 24 to March 24), 18 stock market jumps were recorded, 16 to 18 of which were in response to \"bad news\" about corona pandemic. Therefore, corona pandemic is a source of systematic risk and there is a need for further research on the financial effects of corona pandemic development [2]. This article deals specifically with the relationship between oil prices and the Iranian stock market due to the prevalence of the corona pandemic.\u003c/p\u003e\n\u003cp\u003eIn addition, the role of stock markets in developing economies has become more important since the global crises, and their economies are becoming more involved in the international oil market [6]. The last decade has seen dramatic changes in crude oil prices. One of the most important is the sharp drop in prices in 2008. Resumption of the upward trend after the financial crisis due to oversupply and declining global demand for energy goods. [7]. Ferreira and et al [8] found that stock markets are now more exposed to oil price fluctuation than before the 2008 financial crisis.\u003c/p\u003e\n\u003cp\u003eTo review the relationship between oil prices and the stock market index with the outbreak of the corona pandemic, this article uses data from February 19, 2020 to August 21, 2020. The data are Brent oil prices, the Iranian stock index and some indicators related to the corona pandemic news and the method used in this research is vector autoregression regression.\u003c/p\u003e\n\u003cp\u003eThe rest of the paper is arranged as follows. Section 2 reviews related works with most focus on the research for COVID-19 and relationship between stock market and oil price. Section 3 discusses in details about methodology and used data. Section 4 describes the results of some tests and impulse response functions and variance decompositions. Section 5 evolves this work with a brief of key findings.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#_ftnref1\" name=\"_ftn1\"\u003e[3]\u003c/a\u003e Coronavirus disease 2019\u003c/p\u003e"},{"header":"Literature Review","content":"\u003cp\u003eOil price shocks and the reaction of monetary policy by the oil producer have been an important topic of theory in modern literature.\u003c/p\u003e\n\u003cp\u003eJammazi et al [9] showed significant bidirectional causal relations between oil and stock markets at the different time horizons for France, Germany, Italy, Spain, the UK and the US. Xu and et al [10] showed that strong evidence of asymmetries in volatility shocks between the oil and stock markets due to bad volatility. Also, Bahmani et al. [11] studied asymmetric causality not only from oil price to stock returns but also from stock returns to oil price. They found that an increase in oil price causes returns of three sectors of the U.S. economy, while a decrease in oil price causes returns of four sectors of the U.S. economy, all in the short run.\u003c/p\u003e\n\u003cp\u003eDelpachitra et al [12] examined the economic outcomes of oil price shocks and supply of oil while allowing for interaction between domestic and foreign monetary policy. They concluded that domestic monetary policy is a important channel that computes for over 40% of discounted variation in domestic output across a 4-year horizon after an oil shock. In contrast, US monetary policy is less important in transmitting oil price shocks to the oil-exporting economy through the international channel. Also, K\u0026ouml;se and \u0026Uuml;nal [13] studied the impact of oil price shocks on the stock exchanges of three countries in the Caspian Basin\u0026nbsp;\u0026minus; Iran, Kazakhstan and Russia. The results showed, in these three countries, the impact of negative oil price shocks on the stock market was greater than the positive shocks. The response of the stock exchanges in the three countries to negative oil shocks was highly significant. Bakas and Triantafyllou [14] showed the impact of economic uncertainty about global pandemics on the volatility of the broad commodity price index also on the sub-indexes of crude oil and gold. The conclusion of their study showed that uncertainty related to pandemics have a negative impact on the volatility of commodity markets and especially on crude oil market, while the effect on gold market is positive but less significant.\u003c/p\u003e\n\u003cp\u003eMokni [15] studied the dynamic reaction of a set of oil-related countries\u0026rsquo; stock markets to oil price shocks. He found that the stock returns react more to supply shocks than supply shocks. In addition, the impact of supply shocks on stock returns is generally limited and negative, while aggregate demand shocks have a positive effect on almost all stock returns. Oil demand shocks have positive effects on oil exporter stock returns and negative effects on oil-importing countries, except the Chinese market. Engelhardt et al [16] showed while the covid-19 was driven by news attention or rational expectations about the pandemic\u0026rsquo;s economic impact. Using a sample of 64 national stock markets, which account for 94% of the world's gross domestic product, they present that the fall in stock markets is largely accompanied by more attention to news and less than reasonable expectations. Basher et al [17] concluded the effect that oil market shocks have on stock prices in the fall of the oil exporter is for both domestic and international investors. They studied the nonlinear relationship of oil price shocks with stock market returns in major oil-exporting countries in a multi-factor Markov-switching framework. A portfolio that uses the possibility of Markov switching to move between low-volatility stocks and volatile T-banknotes works better than a buy-and-hold strategy for some countries.\u003c/p\u003e\n\u003cp\u003eSalisu and et al [18] found the impact of own and cross oil price and stock prices shocks during the post-announcement of COVID-19 to be more pronounced for oil and stocks albeit with a larger impact for the former. Azimli [19] investigated the impact of the corona pandemic on the degree and structure of risk-return dependence in the US. Following the COVID-19 outbreak, degree of dependence among returns and market portfolio have increased in the higher quantiles.\u003c/p\u003e\n\u003cp\u003eLy\u0026oacute;csa and Moln\u0026aacute;r [20] use a nonlinear autoregressive model to show that abnormal Google searches related to COVID-19. Al-Awadhi et al [21] studied whether contagious infectious diseases have effect on stock market outcomes. They examined the effect of the COVID-19 virus by using panel data analysis on the Chinese stock market. They concluded that both the daily growth in total confirmed cases and in total cases of death caused by COVID-19 have strong negative effects on stock returns across all companies. He et al. [22] showed the impact of the corona pandemic on the stock prices of some Chinese industries. They concluded that the pandemic negatively impacted stock prices on the Shanghai Stock Exchange, whiles it positively effected the stock prices on the Shenzhen Stock Exchange.\u003c/p\u003e\n\u003cp\u003eAshraf [23] examined the stock markets\u0026rsquo; response to the COVID-19 pandemic. He showed that stock markets response more proactively to the growth in number of COVID-19 confirmed cases as compared to the growth in number of deaths. He also shows negative market response was strong during early days of confirmed cases. He also finds that stock markets quickly react to COVID-19 pandemic and this reaction is different over time depending on the phase of outbreak. Liu et al. [24] examined the short-term effects of the corona pandemics on 21 major stock indices. Using the study event method, they concluded that these indices fell rapidly after the corona outbreak. Indices of Asian countries experienced lower negative returns compared to other countries. Topcua and Gulalb [25] studied the impact of COVID-19 on emerging stock markets. The findings display that the negative impact of pandemic on emerging stock markets has piecemeal fallen and then begun to pull in. The outbreak effects the highest in Asian emerging markets whereas emerging markets in Europe have experienced the lowest. Khantavit [26] performed the stock market response test to COVID-19 using the event study method. The results of this study showed that the stock returns of the world, France, Germany, Italy, Spain, the United States, China, the Philippines and Thailand to the Covid-19 pandemic have been significant and negative. In these countries, reactions to the widespread media coverage of the COVID-19 have been greater than the events and situations taking place. In other words, the markets' reaction to the old news was greater than the new news. Hanke et al. [27] using the risk-neutral densities of six world-famous stock indexes to assess the stock market's preparation for economic shocks, concluded that financial markets had failed to mitigate the major economic effects of COVID-19 predict until late February. This behavior in the market lasts until about mid-March, but from mid-March onwards, market behavior changes. They found that stock markets in countries with lower mortality (Japan, Germany, and the US) are more optimistically than those with higher mortality (France, Italy). Ali et al. [28] investigated the impact of COVID-19 on different financial securities and compared the situation of China and other countries but paid less attention to industry heterogeneity. Qin et al. [29] investigated the impact of the pandemic on oil markets. Liu et al. [30] studied the impact of COVID-19 on crude oil prices and stock prices in the US.\u003c/p\u003e\n\u003cp\u003eAccording to the above literature, the prevalence of corona pandemic has undoubtedly overshadowed the relationships of economic variables. Therefore, it seems necessary to study its effects on various economic relations. This study is the first comprehensive study on the effects of the corona pandemic on the relationship between oil prices and the stock market in Iran. Using the daily statistics of the variables used, the results of the VAR model estimation are examined in the next section.\u003c/p\u003e"},{"header":"Data And Methodology","content":"\u003cp\u003eThe main purpose of this article is to investigate the effect of the corona pandemic on the relationship between oil prices and the total Iranian stock index. The sample period of February 20, 2020 to August 21,2020. The reasons for choosing this period are, firstly, the existence of daily required data in statistical centers, secondly, the existence of oil price shock in this period and thirdly, the outbreak of corona pandemic in the sample selected period. Also, daily data show the relationship between the independent variable and the dependent variable due to the high frequency. Daily data have also been used in the studies of Cepoi [31], Kocaarslan and Soytas [32] and Mensi et al. [33] who have done a topic related to the subject of the present study. Model variables with the following symbols and definitions are included in the model:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 1-\u0026nbsp;\u003c/em\u003e\u003cstrong\u003eVariables introduction\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"208\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"199\"\u003e\n\u003cp\u003eStatement\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"217\"\u003e\n\u003cp\u003eResource\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"208\"\u003e\n\u003cp\u003eTepix\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"199\"\u003e\n\u003cp\u003eTehran stock Index\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"217\"\u003e\n\u003cp\u003ewww.tse.ir\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"208\"\u003e\n\u003cp\u003eBOP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"199\"\u003e\n\u003cp\u003eBrent oil price\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"217\"\u003e\n\u003cp\u003ewww.eia.gov\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"208\"\u003e\n\u003cp\u003eCovid\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"199\"\u003e\n\u003cp\u003eTotal Corona confirmed cases\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"217\"\u003e\n\u003cp\u003ewww.behdasht.gov.ir\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"208\"\u003e\n\u003cp\u003eGold\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"199\"\u003e\n\u003cp\u003eWorld gold price\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"217\"\u003e\n\u003cp\u003ewww.federalreserve.gov\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"208\"\u003e\n\u003cp\u003eMHI\u003ca href=\"#_ftn1\" name=\"_ftnref1\"\u003e[4]\u003c/a\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"199\"\u003e\n\u003cp\u003eThis Index showes the percentage of news talking about the novel coronavirus. Values range between 0 and 100\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"217\"\u003e\n\u003cp\u003ecoronavirus.ravenpack.com\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"208\"\u003e\n\u003cp\u003ePanic\u003ca href=\"#_ftn2\" name=\"_ftnref2\"\u003e[5]\u003c/a\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"199\"\u003e\n\u003cp\u003eThis Index showes the level of news chatter that makes reference to panic or hysteria and coronavirus. Values range between 0 and 100.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"217\"\u003e\n\u003cp\u003ecoronavirus.ravenpack.com\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"208\"\u003e\n\u003cp\u003eFake_news\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"199\"\u003e\n\u003cp\u003eThis Index showes the level of media chatter about the novel virus that makes reference to misinformation or fake news alongside COVID-19. Values range between 0 and 100\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"217\"\u003e\n\u003cp\u003ecoronavirus.ravenpack.com\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"208\"\u003e\n\u003cp\u003eMediaco\u003ca href=\"#_ftn3\" name=\"_ftnref3\"\u003e[6]\u003c/a\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"199\"\u003e\n\u003cp\u003eThis Index showes the percentage of all news sources covering the topic of the novel coronavirus. Values range between 0 and 100\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"217\"\u003e\n\u003cp\u003ecoronavirus.ravenpack.com\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"208\"\u003e\n\u003cp\u003eSentim\u003ca href=\"#_ftn4\" name=\"_ftnref4\"\u003e[7]\u003c/a\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"199\"\u003e\n\u003cp\u003eThis Index showes the level of sentiment across all entities mentioned in the news alongside the coronavirus. The index ranges between -100 and 100\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"217\"\u003e\n\u003cp\u003ecoronavirus.ravenpack.com\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"208\"\u003e\n\u003cp\u003eInfo\u003ca href=\"#_ftn5\" name=\"_ftnref5\"\u003e[8]\u003c/a\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"199\"\u003e\n\u003cp\u003eThis Index shows the percentage of all entities (places, companies, etc.) that are somehow linked to COVID-19. Values range between 0 and 100\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"217\"\u003e\n\u003cp\u003ecoronavirus.ravenpack.com\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eIn this paper, Vector Autoregressive (VAR) model is used to analyze the relationship between variables. Vector autoregression model is one of the successful and flexible models in multivariate time series analysis. In this model, the effect of unexpected shocks is also investigated. This effect is usually determined by examining the impulse response functions and analysis of variance. To estimate the model to achieve the results, it is necessary to go through several steps in all research. There are some tests to examine the model. The following sections include these tests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEmpirical Results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo examine the stationary of the variables, some unit root tests, augmented dickey fuller, Phillips-perron and breakpoint were used. The results of the unit root test of variables are shown in Table 2.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 2\u0026nbsp;\u003c/em\u003e\u003cstrong\u003e\u0026nbsp;Unit root test\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" width=\"0\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" width=\"77\"\u003e\n\u003cp\u003evariable\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"212\"\u003e\n\u003cp\u003eADF test\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"175\"\u003e\n\u003cp\u003eBP test\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"198\"\u003e\n\u003cp\u003ePP test\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e1st difference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003elevel\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e1st difference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003elevel\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e1st difference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003elevel\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003eBOP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e-13.04838\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e-2.77307\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e-14.19636\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e-4.898668\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e-13.0571\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e-2.836142\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003ecovid\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e-14.0783\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e-2.702396\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e-14.77559\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e-4.208126\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e-14.04418\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e-2.76927\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003eTepix\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e-10.08292\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e-1.614724\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e-11.387\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e-2.708662\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e-10.85715\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e-1.614724\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003eMHI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e-8.057414\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e-7.692997\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e-18.49894\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e-8.564565\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e-21.23531\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e-7.692997\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003egold\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e-12.74439\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e-1.278322\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e-14.0478\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e-3.127227\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e-12.79557\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e-1.604293\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003efake_news\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e-11.46549\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e-9.640716\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e-18.58898\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e-10.98816\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e-41.22403\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e-9.544499\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003emediaco\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e-10.83647\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e-12.85037\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e-33.56785\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e-923.601\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e-166.3668\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e-12.85037\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003epanic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e-9.371086\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e-10.44197\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e-20.76069\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e-17.25826\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e-58.60437\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e-10.46091\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003esentim\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e-6.324472\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e-3.422174\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e-14.75188\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e-4.401719\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e-14.27376\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e-3.569898\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003einfo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e-9.282874\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e-7.925341\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e-20.31271\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e-8.799036\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e-24.353\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e-7.925341\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: Researcher findings\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 3\u003c/em\u003e\u0026nbsp;\u003cstrong\u003e- Final results of unit root test\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" width=\"0\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" width=\"95\"\u003e\n\u003cp\u003evariable\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"184\"\u003e\n\u003cp\u003eADF test\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"202\"\u003e\n\u003cp\u003eBP test\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"209\"\u003e\n\u003cp\u003ePP test\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e1st difference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003elevel\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e1st difference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003elevel\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e1st difference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003elevel\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003eBOP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003estationary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003enonstationary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003estationary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003enonstationary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003estationary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003enonstationary\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003ecovid\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003estationary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003enonstationary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003estationary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003enonstationary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003estationary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003enonstationary\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003eTepix\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003estationary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003enonstationary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003estationary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003enonstationary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003estationary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003enonstationary\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003eMHI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003estationary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003estationary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003estationary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003estationary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003estationary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003estationary\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003egold\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003estationary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003enonstationary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003estationary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003enonstationary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003estationary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003enonstationary\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003efake_news\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003estationary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003estationary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003estationary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003estationary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003estationary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003estationary\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003emediaco\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003estationary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003estationary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003estationary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003estationary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003estationary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003estationary\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003epanic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003estationary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003estationary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003estationary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003estationary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003estationary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003estationary\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003esentim\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003estationary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003enonstationary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003estationary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003estationary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003estationary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003enonstationary\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003einfo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003estationary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003enonstationary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003estationary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003estationary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003estationary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003estationary\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: Researcher findings\u003c/p\u003e\n\u003cp\u003eIn this paper, the optimal lag is determined by Akaike information criterion (AIC) Final prediction error (FPR) and Hannan-Quinn (HQ). According to the table below, the optimal lag for the model is lag one.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 4\u003c/em\u003e\u0026nbsp;\u003cstrong\u003eVAR Lag Order Selection Criteria\u003c/strong\u003e\u003c/p\u003e\n\u003ctable width=\"0\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" width=\"71\"\u003e\n\u003cp\u003eLag\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"80\"\u003e\n\u003cp\u003eLogL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"79\"\u003e\n\u003cp\u003eLR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"83\"\u003e\n\u003cp\u003eFPE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"77\"\u003e\n\u003cp\u003eAIC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"79\"\u003e\n\u003cp\u003eSC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"88\"\u003e\n\u003cp\u003eHQ\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"15\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e-8994.284\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003eNA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003e1.79E+36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003e111.8545\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e112.0458\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003e111.9322\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e-7717.172\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e2379.711\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003e\u0026nbsp; 8.01e+29*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003e97.23195*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e\u0026nbsp; 99.33726*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003e\u0026nbsp; 98.08679*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e-7632.562\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e\u0026nbsp; 147.1490*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003e9.82E+29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003e97.42312\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e101.4423\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003e99.05509\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e-7559.779\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e117.5374\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003e1.42E+30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003e97.76123\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e103.6944\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003e100.1703\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e-7484.862\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e111.6772\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003e2.08E+30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003e98.07282\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e105.9199\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003e101.259\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e-7415.353\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e94.98047\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003e3.39E+30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003e98.4516\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e108.2126\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003e102.4149\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e-7351.376\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e79.47513\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003e6.30E+30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003e98.89908\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e110.574\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003e103.6396\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e-7240.669\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e123.7719\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003e7.09E+30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003e98.76607\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e112.3549\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003e104.2837\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"80\"\u003e\n\u003cp\u003e-7134.162\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e105.8457\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003e9.36E+30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003e98.68524\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e114.188\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003e104.98\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: Researcher findings\u003c/p\u003e\n\u003cp\u003eCointegration test is to observe the long-term equilibrium relationship between the non-stationary variables. In this study, cointegration tests were accomplished out using the Johansen\u0026rsquo;s cointegration method. Table 5 shows that there are there are at least 3 long run relationships. The trace statistic value proves it and the maximum eigenvalue that greater than the critical value.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 5-\u0026nbsp;\u003c/em\u003e\u003cstrong\u003e\u0026nbsp;Johansen cointegration test result\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" width=\"0\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" width=\"147\"\u003e\n\u003cp\u003eHypothesized No. of CE(s)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"91\"\u003e\n\u003cp\u003eTrace Statistic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"109\"\u003e\n\u003cp\u003e0.05 Critical Value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"87\"\u003e\n\u003cp\u003eProb\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"0\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"0\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003eNone *\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"91\"\u003e\n\u003cp\u003e450.2339\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e239.2354\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"0\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003eAt most 1 *\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"91\"\u003e\n\u003cp\u003e329.4797\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e197.3709\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"0\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003eAt most 2 *\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"91\"\u003e\n\u003cp\u003e233.4116\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e159.5297\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"0\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003eAt most 3 *\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"91\"\u003e\n\u003cp\u003e150.0963\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e125.6154\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e0.0007\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"0\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003eAt most 4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"91\"\u003e\n\u003cp\u003e93.14551\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e95.75366\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e0.0746\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"0\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003eAt most 5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"91\"\u003e\n\u003cp\u003e57.98253\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e69.81889\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e0.3025\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"0\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003eAt most 6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"91\"\u003e\n\u003cp\u003e32.22011\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e47.85613\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e0.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"0\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003eAt most 7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"91\"\u003e\n\u003cp\u003e13.86497\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e29.79707\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e0.8481\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"0\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003eAt most 8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"91\"\u003e\n\u003cp\u003e5.254209\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e15.49471\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e0.7812\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"0\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003eAt most 9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"91\"\u003e\n\u003cp\u003e1.545217\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e3.841466\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e0.2138\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"0\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: Researcher findings\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 6\u0026nbsp;\u003c/em\u003e\u003cstrong\u003eVariance Decomposition of Tepix\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" width=\"0\"\u003e\n\u003ctbody\u003e\n\u003ctr style=\"height: 13.3267px;\"\u003e\n\u003ctd style=\"height: 13.3267px;\" colspan=\"5\" width=\"255\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 13.3267px;\" width=\"72\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 13.3267px;\" width=\"72\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 13.3267px;\" width=\"72\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 13.3267px;\" width=\"72\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 13.3267px;\" width=\"72\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 13.3267px;\" width=\"78\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 13.3267px;\" width=\"72\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 13.3267px;\" width=\"72\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 13.3267px;\" width=\"0\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 14px;\"\u003e\n\u003ctd style=\"height: 29px;\" rowspan=\"2\" width=\"47\"\u003e\n\u003cp\u003e\u0026nbsp;Period\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 29px;\" colspan=\"2\" rowspan=\"2\" width=\"64\"\u003e\n\u003cp\u003eS.E.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 29px;\" rowspan=\"2\" width=\"80\"\u003e\n\u003cp\u003eTepix\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 29px;\" rowspan=\"2\" width=\"64\"\u003e\n\u003cp\u003esentim\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 29px;\" rowspan=\"2\" width=\"72\"\u003e\n\u003cp\u003epanic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 29px;\" rowspan=\"2\" width=\"72\"\u003e\n\u003cp\u003emediaco\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 29px;\" rowspan=\"2\" width=\"72\"\u003e\n\u003cp\u003eMHI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 29px;\" rowspan=\"2\" width=\"72\"\u003e\n\u003cp\u003einfo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 29px;\" rowspan=\"2\" width=\"72\"\u003e\n\u003cp\u003egold\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 29px;\" rowspan=\"2\" width=\"78\"\u003e\n\u003cp\u003efake_news\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 29px;\" rowspan=\"2\" width=\"72\"\u003e\n\u003cp\u003ecovid\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 29px;\" rowspan=\"2\" width=\"72\"\u003e\n\u003cp\u003ebop\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 14px;\" width=\"0\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 15px;\"\u003e\n\u003ctd style=\"height: 15px;\" width=\"0\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" colspan=\"2\" width=\"52\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"59\"\u003e\n\u003cp\u003e26676.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"80\"\u003e\n\u003cp\u003e100\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"64\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"78\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"0\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" colspan=\"2\" width=\"52\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"59\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"80\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"64\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"78\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"0\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" colspan=\"2\" width=\"52\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"59\"\u003e\n\u003cp\u003e37950.51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"80\"\u003e\n\u003cp\u003e99.42507\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"64\"\u003e\n\u003cp\u003e0.006277\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.032212\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.045005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.124134\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.07671\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.00433\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"78\"\u003e\n\u003cp\u003e0.274387\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.010544\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.001332\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"0\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" colspan=\"2\" width=\"52\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"59\"\u003e\n\u003cp\u003e46780.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"80\"\u003e\n\u003cp\u003e98.91242\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"64\"\u003e\n\u003cp\u003e0.008818\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.069344\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.033678\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.297816\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.219076\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.014669\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"78\"\u003e\n\u003cp\u003e0.401205\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.037272\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.005702\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"0\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" colspan=\"2\" width=\"52\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"59\"\u003e\n\u003cp\u003e54354.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"80\"\u003e\n\u003cp\u003e98.45556\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"64\"\u003e\n\u003cp\u003e0.010914\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.104309\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.025074\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.477893\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.365123\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.028743\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"78\"\u003e\n\u003cp\u003e0.441882\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.077093\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.013413\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"0\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" colspan=\"2\" width=\"52\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"59\"\u003e\n\u003cp\u003e61131.43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"80\"\u003e\n\u003cp\u003e98.04455\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"64\"\u003e\n\u003cp\u003e0.01402\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.132165\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.022456\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.643872\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.498391\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.044725\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"78\"\u003e\n\u003cp\u003e0.448544\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.126725\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.024553\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"0\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" colspan=\"2\" width=\"52\"\u003e\n\u003cp\u003e30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"59\"\u003e\n\u003cp\u003e166146.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"80\"\u003e\n\u003cp\u003e93.86177\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"64\"\u003e\n\u003cp\u003e0.483191\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.117418\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.072221\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e1.122567\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e1.160545\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.404458\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"78\"\u003e\n\u003cp\u003e0.28934\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e1.480923\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e1.007572\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"0\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" colspan=\"2\" width=\"52\"\u003e\n\u003cp\u003e31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"59\"\u003e\n\u003cp\u003e169498.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"80\"\u003e\n\u003cp\u003e93.77598\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"64\"\u003e\n\u003cp\u003e0.499799\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.113831\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.072071\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e1.109439\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e1.159249\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.414137\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"78\"\u003e\n\u003cp\u003e0.286324\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e1.511819\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e1.057347\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"0\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" colspan=\"2\" width=\"52\"\u003e\n\u003cp\u003e32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"59\"\u003e\n\u003cp\u003e172824.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"80\"\u003e\n\u003cp\u003e93.69319\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"64\"\u003e\n\u003cp\u003e0.515611\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.110375\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.071871\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e1.096435\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e1.157698\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.423366\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"78\"\u003e\n\u003cp\u003e0.283424\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e1.541124\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e1.106902\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"0\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" colspan=\"2\" width=\"52\"\u003e\n\u003cp\u003e33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"59\"\u003e\n\u003cp\u003e176125.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"80\"\u003e\n\u003cp\u003e93.61329\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"64\"\u003e\n\u003cp\u003e0.530641\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.10705\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.071626\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e1.08359\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e1.15593\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.432148\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"78\"\u003e\n\u003cp\u003e0.280635\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e1.568913\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e1.156172\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"0\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" colspan=\"2\" width=\"52\"\u003e\n\u003cp\u003e34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"59\"\u003e\n\u003cp\u003e179402.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"80\"\u003e\n\u003cp\u003e93.53619\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"64\"\u003e\n\u003cp\u003e0.544905\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.103852\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.071343\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e1.07093\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e1.153983\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.440487\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"78\"\u003e\n\u003cp\u003e0.277951\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e1.59526\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e1.205097\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"0\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" colspan=\"2\" width=\"52\"\u003e\n\u003cp\u003e35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"59\"\u003e\n\u003cp\u003e182658\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"80\"\u003e\n\u003cp\u003e93.46179\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"64\"\u003e\n\u003cp\u003e0.558422\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.100777\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.071027\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e1.058478\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e1.151886\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.448387\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"78\"\u003e\n\u003cp\u003e0.275367\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e1.620238\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e1.253623\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"0\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" colspan=\"2\" width=\"52\"\u003e\n\u003cp\u003e36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"59\"\u003e\n\u003cp\u003e185892.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"80\"\u003e\n\u003cp\u003e93.39001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"64\"\u003e\n\u003cp\u003e0.571216\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.097822\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.070683\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e1.04625\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e1.149667\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.455856\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"78\"\u003e\n\u003cp\u003e0.27288\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e1.643914\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e1.301701\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"0\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" colspan=\"2\" width=\"52\"\u003e\n\u003cp\u003e37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"59\"\u003e\n\u003cp\u003e189106.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"80\"\u003e\n\u003cp\u003e93.32076\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"64\"\u003e\n\u003cp\u003e0.583309\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.094983\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.070314\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e1.034257\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e1.147348\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.4629\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"78\"\u003e\n\u003cp\u003e0.270485\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e1.666357\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e1.349287\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"0\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" colspan=\"2\" width=\"52\"\u003e\n\u003cp\u003e38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"59\"\u003e\n\u003cp\u003e192302.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"80\"\u003e\n\u003cp\u003e93.25395\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"64\"\u003e\n\u003cp\u003e0.594729\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.092256\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.069925\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e1.022511\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e1.14495\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.46953\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"78\"\u003e\n\u003cp\u003e0.268179\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e1.68763\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e1.396342\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"0\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" colspan=\"2\" width=\"52\"\u003e\n\u003cp\u003e39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"59\"\u003e\n\u003cp\u003e195480.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"80\"\u003e\n\u003cp\u003e93.1895\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"64\"\u003e\n\u003cp\u003e0.605501\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.089637\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.069518\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e1.011016\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e1.142489\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.475754\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"78\"\u003e\n\u003cp\u003e0.265958\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e1.707794\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e1.442832\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"0\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" colspan=\"2\" width=\"52\"\u003e\n\u003cp\u003e40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"59\"\u003e\n\u003cp\u003e198641.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"80\"\u003e\n\u003cp\u003e93.12734\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"64\"\u003e\n\u003cp\u003e0.615653\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.087121\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.069096\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.999777\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e1.139982\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e0.481583\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"78\"\u003e\n\u003cp\u003e0.263819\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e1.726908\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"72\"\u003e\n\u003cp\u003e1.488726\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"0\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: Researcher findings\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable7 \u003c/em\u003e\u003cstrong\u003e- Variance Decomposition of bop\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" width=\"0\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\" width=\"253\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"0\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" width=\"52\"\u003e\n\u003cp\u003e\u0026nbsp;Period\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"68\"\u003e\n\u003cp\u003eS.E.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"68\"\u003e\n\u003cp\u003eTepix\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"64\"\u003e\n\u003cp\u003esentim\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"72\"\u003e\n\u003cp\u003epanic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"64\"\u003e\n\u003cp\u003emediaco\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"64\"\u003e\n\u003cp\u003eMHI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"64\"\u003e\n\u003cp\u003einfo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"64\"\u003e\n\u003cp\u003egold\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"75\"\u003e\n\u003cp\u003efake_news\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"64\"\u003e\n\u003cp\u003ecovid\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"64\"\u003e\n\u003cp\u003ebop\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"0\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"0\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e4.763361\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e0.05428\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e0.347567\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003e0.006679\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e0.203992\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e0.037375\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e1.707587\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e0.41721\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e0.852942\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e0.022763\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e96.34961\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"0\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e6.340502\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e0.039132\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e0.185809\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003e0.93393\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e0.132232\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e0.71332\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e1.233928\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e0.246961\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e0.850452\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e0.036076\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e95.62816\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"0\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e7.287228\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e0.027223\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e0.135238\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003e1.636722\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e0.187122\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e1.490961\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e0.859076\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e0.172015\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e0.97162\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e0.060817\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e94.45921\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"0\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e7.913059\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e0.032147\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e0.139379\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003e2.189311\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e0.286014\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e2.271526\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e0.677511\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e0.171024\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e0.983939\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e0.094716\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e93.15443\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"0\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e8.349105\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e0.055375\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e0.183526\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003e2.620996\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e0.395368\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e2.995302\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e0.611356\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e0.229758\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e0.951017\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e0.136136\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e91.82117\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"0\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e9.944225\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e4.717895\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e4.084736\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003e3.595267\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e1.219549\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e6.502887\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e1.168386\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e7.489288\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e0.469627\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e1.841331\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e68.91103\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"0\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e9.950775\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e5.006479\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e4.184458\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003e3.562898\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e1.220056\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e6.479821\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e1.172189\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e7.752679\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e0.464028\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e1.896786\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e68.26061\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"0\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e9.956583\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e5.298516\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e4.277519\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003e3.531102\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e1.219921\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e6.455267\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e1.17543\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e8.004356\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e0.458763\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e1.950036\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e67.62909\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"0\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e9.961729\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e5.593633\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e4.36416\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003e3.499965\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e1.219212\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e6.429499\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e1.178169\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e8.24416\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e0.453802\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e2.001089\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e67.01631\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"0\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd 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width=\"64\"\u003e\n\u003cp\u003e9.265041\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e0.432724\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e2.224407\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e64.22315\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"0\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e9.982291\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e7.411055\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e4.763631\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003e3.329403\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e1.206074\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e6.260639\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e1.186703\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e9.434482\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e0.42911\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e2.262979\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e63.71592\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"0\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e9.984466\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"68\"\u003e\n\u003cp\u003e7.719214\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e4.812605\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003e3.303814\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e1.202819\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e6.231461\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e1.187155\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e9.592876\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e0.425658\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e2.299643\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e63.22476\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"0\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe results of variance decompositions show that most of the changes in the variables after the shocks on them are explained by those variables themselves. Also, the change of the total stock index variable after the shock is first explained by the variable itself, then by total corona confirmed cases, oil price, Coronavirus Infodemic index, Coronavirus Media Hype Index, Coronavirus sentiment index, gold price, Coronavirus Fake_news, the Panic Index and finally Coronavirus Media Coverage Index, respectively. As it is clear from the numbers in the table above, the number of daily patients has the most explanation.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#_ftnref1\" name=\"_ftn1\"\u003e[4]\u003c/a\u003e Coronavirus Media Hype Index\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#_ftnref2\" name=\"_ftn2\"\u003e[5]\u003c/a\u003e Coronavirus Panic Index\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#_ftnref3\" name=\"_ftn3\"\u003e[6]\u003c/a\u003e Coronavirus Media Coverage Index\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#_ftnref4\" name=\"_ftn4\"\u003e[7]\u003c/a\u003e Coronavirus Sentiment Index\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"#_ftnref5\" name=\"_ftn5\"\u003e[8]\u003c/a\u003e Coronavirus Infodemic Index\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe main propose of this paper is to understand the effects of Corona pandemic on the relationship between oil price and Iran stock index by some indicators, which are introduced and prepared for each country in raven pack data base and this paper has used Iran\u0026rsquo;s indicators about Corona pandemic.\u003c/p\u003e\n\u003cp\u003eFinding inverse and direct relationship between stock index and the indicators of Corona pandemic, In terms of oil prices in regression, account for effect Corona pandemic in the stock market and in general the feelings of investors in stock market of Iran.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThese analyzes supply strong elements to figure out how, after a health crisis as corona pandemic, it has been feasible to achieve the relationship between this crisis and the stock market of Iran.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe current paper investigates examines the relationship between oil prices and the Tehran stock index with the presence of the Corona pandemic. To examine the impact of Corona, the indicators have been used. These indicators include the number of total Corona confirmed cases, fake news about the corona, Coronavirus Media Coverage Index, the panic index, Coronavirus Media Hype Index, the sentiment index and the infodemic index. Descriptions related to each of these indicators are provided in the data introduction section.\u003c/p\u003e\n\u003cp\u003eWhile oil prices shocks happen due to business cycle fluctuations and some other reasons like political reasons, occur; The correlations between changes in Brent oil prices and stock market indices tend to be affected by named corona indexes.\u003c/p\u003e\n\u003cp\u003eUsing the data of the period from February 20, 2020 to August 21,2020 and using the vector autoregression model, we came to the conclusion that first of all the variables used in the article except the variables Media Hype Index, fake_news Index, Media Coverage Index and Panic with once differentiation is sustained, the variables mentioned above are also sustained. The results of the optimal lag also suggested the optimal 1 lag. Using an optimal lag, the Johansson test also proved four significant long-run relationships between the variables.\u003c/p\u003e\n\u003cp\u003eThis finding showed that the dynamics of stock markets during the outbreak of the coronavirus cannot be accidental. Capelle and Desroziers [34] research has shown that it is not the pre-crisis state of economies that causes current stock market reaction to the corona pandemic, but the adoption of some major government policies that have led to the dynamism of stock markets. He cited government health policies against the Corona pandemic and government support for companies affiliated with health products and services as examples. The results of the model indicate that the unprecedented rise in the stock market cannot be justified in the short term with the outbreak of the Corona virus.\u003c/p\u003e\n\u003cp\u003eIn this study, the Corona Media Index, which due to the significant negative short-term effect it has on the value of the stock exchange, indicates the impact of the stock market on the news in the days when the Corona news was high. With the increase in the number of social and digital networks, it is possible that this index and similar indicators will have a much higher impact on the value of the stock exchange transaction. Also, over time, this connection will become more logical and transparent. The empirical results of Impulse response functions showed that the response of the stock index to the shock on the Infodemic Index and Media Hype Index is negative. Also, the response of the total index to the total Corona confirmed cases was positive. Against the Fake news Index, it tends to be positive in the short run and to a neutral value in the long run. Regarding the effect of the shock on oil prices and its effect on the total stock index, it can be said that there was a negative relationship from the beginning to the end of the period and a shock on oil prices reduces the stock index. We also found a positive correlation between the oil price response to the shock on the Sentiment Index, Tepix, the Media Coverage Index and the total Corona confirmed cases. And with a positive shock on these indicators, a positive effect on oil prices was observed. A positive shock on the Panic Index, Media Hype Index, the Infodemic Index and gold also had a negative effect on oil prices. Finally, fake news has a positive effect in the short run and a negative effect in the long run, and Media Coverage Index shows a positive effect in the short run and a neutral effect on oil prices in the long run.\u003c/p\u003e\n\u003cp\u003eThis finding implies a mutual risk transmission between oil and stock markets because of the financialization of the crude oil market and the unison movement of oil and stock markets over the last few decades mainly driven by changes in global aggregate demand. The results also show that the causal interactions tend to be stronger at the coarser time scales and are particularly pronounced during periods of economic and financial turmoil such as the recent global financial crisis and European sovereign debt crisis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe authors approve that they consent to participate.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe authors are satisfied with the publication.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eAccess to all references mentioned in the text\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eAuthors are equally involved in writing the article.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eAuthors' information\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eVida Varahrami, economics department, shahid Beheshti university, Tehran, Iran\u003c/p\u003e\n\u003cp\u003eMasoumeh Dadgar, economic and social science faculty, Alzahra university. Tehran, Iran\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRu, Hong ; Yang, Endong ; Zou, Kunru ;. (2020). Combating the COVID-19 Pandemic: The Role of the SARS Imprint. Retrieved from \u003ca href=\"https://ssrn.com/abstract=3641827\"\u003ehttps://ssrn.com/abstract=3641827\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eSharif, Arshian; Aloui, Chaker; Yarovaya, Larisa;. (2020, July). COVID-19 Pandemic, Oil Prices, Stock Market, Geopolitical Risk And Policy Uncertainty Nexus In The US Economy: Fresh Evidence From The Wavelet-Based Approach. \u003cem\u003e70:101496\u003c/em\u003e. Doi:Https://Doi.Org/10.1016/J.Irfa.2020.101496\u003c/li\u003e\n\u003cli\u003eGoodell, John W.;. (2020). COVID-19 And Finance: Agendas For Future Research. \u003cem\u003eFinance Research Letters, 35:101512\u003c/em\u003e. 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The Stock Market And The Economy: Insights From The COVID-19 Crisis. \u003cem\u003eSocial Science Research Network\u003c/em\u003e.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Corona virus, oil price, stock market index, VAR, Covid-19, Brent oil price, Tehran stock Index, Gold price, Media Hype Index, fake news Index, Media Coverage Index and Panic","lastPublishedDoi":"10.21203/rs.3.rs-409534/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-409534/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis article reviews the relationship between the oil market and the stock market during the Corona outbreak. This study aims to analyze the stock market and the effect of oil prices on this market during the corona pandemic. The hypothesis of this paper is whether while oil prices shocks happen due to business cycle fluctuations and some other reasons like political reasons, occur; The correlations between changes in Brent oil prices and stock market indices tend to be affected by named corona indexes. Forecasting the stock market in each period has been difficult and the value of stock index has been affected by various factors. Among these factors has been the oil and gas sector, especially in countries dependent on the revenue from their sales. On the other hand, the outbreak of Covid-19 pandemic has led to profound changes in both areas. This study examines relationship between Brent oil price and Iran stock market Index during the outbreak of corona pandemic. Research method is, vector autoregression model (VAR) which using daily data covering the period from February 20, 2020 to August 21,2020. The findings of this study suggest that a negative causal effect from Brent oil price changes to the Iran stock market Index. Also, the results of impulse response functions and variance decompositions showed that some corona pandemic indicators have significant effects on the stock index.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eJEL Classification\u003c/strong\u003e: I18, E44, Q4, C5\u003c/p\u003e","manuscriptTitle":"Linkages Between Brent Oil Price And Iran Stock Market: New Evidence From The Corona Pandemic","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-05-04 18:25:55","doi":"10.21203/rs.3.rs-409534/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f0aa4a89-97a5-45fa-b2c1-8adad15bfeb4","owner":[],"postedDate":"May 4th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":4083851,"name":"Econometrics"},{"id":4083852,"name":"Other Economics"}],"tags":[],"updatedAt":"2021-07-25T14:44:40+00:00","versionOfRecord":[],"versionCreatedAt":"2021-05-04 18:25:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-409534","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-409534","identity":"rs-409534","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}
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