key: cord-301348-h21rnyww
authors: Gherghina, Ștefan Cristian; Armeanu, Daniel Ștefan; Joldeș, Camelia Cătălina
title: Stock Market Reactions to COVID-19 Pandemic Outbreak: Quantitative Evidence from ARDL Bounds Tests and Granger Causality Analysis
date: 2020-09-15
journal: Int J Environ Res Public Health
DOI: 10.3390/ijerph17186729
sha: 
doc_id: 301348
cord_uid: h21rnyww

This paper examines the linkages in financial markets during coronavirus disease 2019 (COVID-19) pandemic outbreak. For this purpose, daily stock market returns were used over the period of December 31, 2019–April 20, 2020 for the following economies: USA, Spain, Italy, France, Germany, UK, China, and Romania. The study applied the autoregressive distributed lag (ARDL) model to explore whether the Romanian stock market is impacted by the crisis generated by novel coronavirus. Granger causality was employed to investigate the causalities among COVID-19 and stock market returns, as well as between pandemic measures and several commodities. The outcomes of the ARDL approach failed to find evidence towards the impact of Chinese COVID-19 records on the Romanian financial market, neither in the short-term, nor in the long-term. On the other hand, our quantitative approach reveals a negative effect of the new deaths’ cases from Italy on the 10-year Romanian bond yield both in the short-run and long-run. The econometric research provide evidence that Romanian 10-year government bond is more sensitive to the news related to COVID-19 than the index of the Bucharest Stock Exchange. Granger causality analysis reveals causal associations between selected stock market returns and Philadelphia Gold/Silver Index.

With globalization, urban sprawl, and ecological transformations, contagious disease outbursts turned out to be worldwide risks demanding a joint reply [1] . According to the International Monetary Fund (IMF), coronavirus disease 2019 (COVID-19) generated an economic crisis different from the others [2] for the reason that it is much more multifaceted (interconnections between the economy and the health system), uncertain (the related treatment is established gradually, alongside the measures concerning how to streamline isolation and the means to start over the economy), and has a worldwide character. Both supply and demand reductions occur since individuals work and consume lower, whereas companies diminish their productivity and investment [3] . Hence, Erokhin and Gao [4] explored 45 developing states and established that food security status of individuals and the strength of food supply chains are impacted by Consequently, governments have taken unprecedented actions, respectively fiscal measures figuring to around $8 trillion, whereas central banks injected liquidity getting up to over $6 trillion [5] . The IMF has implemented exceptional measures by doubling its emergency loaning volume to $100 billion and deferring debt outflows for poor nations [6] . Preparing for the economic recovery raised a number of issues such as the way to maintain fiscal stimulus and unconventional monetary policy, managing high unemployment, low interest rates, and preserving financial stability [7] . Hence, market inefficiencies to acquire abnormal returns [73] . On the contrary, Yan, et al. [74] recommended the tourism industry, technology sector, leisure industry, and gold as suitable investments. Li, et al. [75] endorsed health sector in line with Chong, et al. [76] which suggested over SARS to buy medical stocks and sell tourism stocks. In terms of cryptocurrencies, Chen, et al. [77] argued that augmented concerns of the coronavirus caused negative Bitcoin returns and large trading volume, whereas Conlon and McGee [78] advised that it does not perform as a hedge.

With reference to the influence of the pandemic on the enterprise's activities, Mazur, et al. [79] contended that companies reply in various means to the COVID-19 revenue shock because many sectors were locked throughout the quarantine stage. Hence, Xiong, Wu, Hou and Zhang [9] evidenced that companies belonging to sectors that are exposed to the pandemic have significantly lower cumulative abnormal returns, but enterprises with good financial conditions endure less opposing effect of the disease. Nguyen [80] established that energy segment experienced the utmost abnormal negative returns amid all sectors. Fallahgoul [81] established that the financial segment is the most doubtful, whereas health is the most hopeful over the COVID-19 pandemic. He, Sun, Zhang and Li [15] claimed that manufacturing, information technology, education and health-care Chinese sectors remained stable to COVID-19. Gu, et al. [82] found that Chinese manufacturing sector was hardly hit by corona crisis, but construction, information transfer, computer services and software, and health care and social work were positively influenced by COVID-19.

Financial markets worldwide confronted with the flight-to-safety phenomenon which engendered a severe deterioration in asset appraisals and amplified volatility around the world [11] . Baker, Bloom, Davis, Kost, Sammon and Viratyosin [30] stressed that there was no prior illness that determined such daily stock market jumps. Albulescu [83] emphasized that the fatality rate has a positive and very significant influence on financial volatility, whereas Albuquerque, Koskinen, Yang and Zhang [31] found that green stocks are highly valued and register lower volatility and larger trading volumes than the rest of stocks.

Markets are a function of government, hence responding reliant on authority reply [84] . Alfaro, Chari, Greenland and Schott [32] confirmed that a doubling of projected contaminations is linked with a 4 to 11 percent deterioration of aggregate market value. Alber [85] showed that stock market return is influenced by COVID-19 cases more than deaths, as well as by aggregate measures more than new ones. However, attributable to local features, the influence of novel coronavirus may diverge across equity markets [86] . Onali [33] revealed that variations in the amount of cases and deaths in the USA and other highly impacted nations by the coronavirus do not influence stock market returns out of USA, except the number of cases for China. The spread of COVID-19 globally driven an upsurge of yields on sovereign securities more than proportionally in developing and emerging states [36] . Nozawa and Qiu [35] noticed that corporate bonds supplied by companies showing a strong link with China respond more to the quarantine of Wuhan at early 2020. Hence, M.Al-Awadhi, Alsaifi, Al-Awadhi and Alhammadi [29] concluded that the COVID-19 disease negatively influence stock market returns of the companies covered in the Hang Seng Index and Shanghai Stock Exchange Composite Index. Adenomon, Maijamaa and John [34] strengthened that the coronavirus disease negatively influences the stock returns in Nigeria.

On the contrary, there was proved that everyday cases of new contagions have a low adverse effect on the crude oil quotations in the long-term [37] . Albulescu [38] explored whether the COVID-19 and crude oil influence the economic policy uncertainty of the United States and observed no impact when considering the global coronavirus data, but a positive effect when assessing the condition outside China. Sharif, et al. [87] established a unique responsiveness of stock market of USA, related economic policy uncertainty, and geopolitical risk to the joint shocks of the coronavirus and oil instability. For the case of Colombia, Cardona-Arenas and Serna-Gómez [26] argued that the depreciation of national In addition, we have included a wide range of variables that allow us to achieve our goal, such as COVID-19 measures, commodities, currencies, and 10-Year government bond spreads.

In order to gain insights towards the linkages in stock markets during COVID-19 pandemic outbreak, we will use the autoregressive distributed lag (ARDL) model similar Albulescu [37, 38] , Erokhin and Gao [4] , as well as Granger causality test alike Mamaysky [88] . Checking for unit root in ARDL approach is not fundamental in as much as it can examine for the occurrence of cointegration among a set of variables of order I(0) or I(1) or a mixture of them. Hence, the leading benefit of ARDL model consist in its versatility. However, the ARDL methodology impose that no variable should be integrated of second order or I (2) . Therefore, in line with prior research [26, 34, 59] , the augmented Dickey-Fuller (ADF) test will be applied for unit root testing. The null hypothesis of the ADF test claims the presence of unit root in the time series.

The ADF test involves estimating the following equation:

where t denotes the time trend, T signifies the length of the sample, while k is the length of the lag in the dependent variable. Further, ARDL model examines the long and short-term cointegration, being specified as a sole equation framed with adaptable choice of lag extents. The general form of an ARDL (p, q) model is as follows:

The lag orders p and q are established by means of the Akaike Information criteria and may differ over the explanatory variables covered in our quantitative framework.

The Granger causality test can be applied to analyze the causality between variables, as in Mamaysky [88] . The null hypothesis is that w does not Granger-cause z and that z does not Granger-cause w. The following bivariate regressions will be estimated:

w t = α 0 + α 1 w t−1 + · · · + α p w t−p + β 1 z t−1 + · · · + β p z −p + u t (4)

The descriptive statistics of the variables are provided in Table 2 . The distributions of all stock market returns, as well as most of included commodities are negatively skewed. Thus, negative returns are more prevalent than positive returns, supporting a greater likelihood for very high losses. Kurtosis shows the thickness of the tail and highlights a high level of risk for selected stock markets, especially Spain and Italy. In addition, except EUR/CNY and Natural Gas Futures Contract 1, the Jarque-Bera test provides evidence that selected series are not normally distributed. Figure 1 shows the evolution of the number of new cases due to COVID-19, whereas Figure 2 reveals the progress of the number of new death due to COVID-19. There is noticed that USA registers the highest figures in this regard. Source: authors' own calculations. Notes: for the definition of variables, please see Table 1 . Figure 1 shows the evolution of the number of new cases due to COVID-19, whereas Figure 2 reveals the progress of the number of new death due to COVID-19. There is noticed that USA registers the highest figures in this regard. Figure 3 shows the evolution of stock market returns amongst the explored period. There is reinforced the significant volatility, especially for FTSE MIB on March 9, 2020 and March 12, 2020, as well as for Dow Jones Industrial Average on March 16, 2020. In the first two months of 2020, DAX declined by 10.2 percent, CAC 40 dropped by 11.2 percent, whereas FTSE 100 plunged 12.7%. In the same vein, Dow Jones throw down by 11 percent and S&P 500 by 8.6 percent. The Bucharest Stock Exchange also encountered instabilities and registered a decay of 8.6 percent [89] . Capelle-Blancard and Desroziers [90] contended that prior to February 21, stock markets disregarded the pandemic, but over February 23-March 20, the reaction to the rising number of diseased people was strong. As such, Mazur, Dang and Vega [79] emphasized that the failure of stock quotes in March 2020 marked one of the major financial market collapses in history. Baiardi, et al. [91] developed a three-regime switching model and concluded that in 2020 the most common state for the Dow Jones Industrial Average was turbulent. Figure 3 shows the evolution of stock market returns amongst the explored period. There is reinforced the significant volatility, especially for FTSE MIB on March 9, 2020 and March 12, 2020, as well as for Dow Jones Industrial Average on March 16, 2020. In the first two months of 2020, DAX declined by 10.2 percent, CAC 40 dropped by 11.2 percent, whereas FTSE 100 plunged 12.7%. In the same vein, Dow Jones throw down by 11 percent and S&P 500 by 8.6 percent. The Bucharest Stock but over February 23-March 20, the reaction to the rising number of diseased people was strong. As such, Mazur, Dang and Vega [79] emphasized that the failure of stock quotes in March 2020 marked one of the major financial market collapses in history. Baiardi, et al. [91] developed a three-regime switching model and concluded that in 2020 the most common state for the Dow Jones Industrial Average was turbulent. Table 1 . Figure 4 reveals the evolution of oil futures. There is noticed the sharp decline registered on 21 April 2020. Figure 5 shows the progress of Philadelphia Gold/Silver Index returns. Therewith, high volatility is prevailing. Table 1 . Figure 4 reveals the evolution of oil futures. There is noticed the sharp decline registered on 21 April 2020. Figure 5 shows the progress of Philadelphia Gold/Silver Index returns. Therewith, high volatility is prevailing. Table 1 . Table 1 . for the definition of variables, please see Table 1 . Table 3 reveals the correlations among selected variables. There are acknowledged high negative correlations (below −0.7) between the number of new cases and new deaths due to COVID-19 in Italy and crude oil, WTI, as well as NYMEX light sweet crude oil. In case of the number of new cases and Table 1 . Table 3 reveals the correlations among selected variables. There are acknowledged high negative correlations (below −0.7) between the number of new cases and new deaths due to COVID-19 in Italy and crude oil, WTI, as well as NYMEX light sweet crude oil. In case of the number of new cases and new deaths due to COVID-19 in China, there are not recorded high correlations with the included measures. Therewith, high positive correlations (over 0.7) are registered amongst the stock market returns, except SSE 100 (China).

Non-stationary variables lead to inadequate results, which means insignificant results. The verification of the stationarity of the selected data is performed through ADF stationarity test. This test is most commonly used to confirm the stationarity of a data series. Table 4 shows the results of the ADF test at the level and in the first difference, as well as the level of integration of the stock indices.

The outcomes of ADF test provide support that all covered stock indices are stationary at the first difference, showing an integration order of I(1), except the stock market index from the Shanghai Stock Exchange. We also notice that the indicators related to the evolution of COVID-19 for the most affected regions, China and Italy, show a mixed integration order (I(0)and I(1)).

After studying the stationary of the data series and due to the mixed results, we conclude that the ARDL model is the most appropriate for exploring the linkages between variables. Further, the purpose is to assess whether new cases and new deaths due to COVID-19 in China and Italy, along with Chinese and Italian stock market returns, several commodities, and currencies are related to the Romanian stock market as measured by BET index return and Romania 10-year bond yield.

The ARDL (autoregressive distributed lag) model is used especially when the variables I(0) and I(1) are integrated. For the accurate choice of the ARDL model that would allow us to research the relationships that are established between variables, it is imperative to choose the correct number of lags. Therefore, we will analyze the Akaike information criteria (AIC) to select the optimal lags for the variables included in the ARDL model. We will apply the criteria graph, which will indicate the suitable lags for the ARDL model and the lowest value is preferred. Figure 6 shows the results of criteria graph for the ARDL model that takes into account the number of new cases and new deaths in China, both for the BET stock index return and for the Romanian Government bond (10Y).

According to the results, in total, 1,562,500 ARDL model specifications were considered for each of the four cases given the information related to COVID-19 in China. The top 20 results are presented in the criteria graph.

Further, Table 5 summarizes the selected lags for the model Romania and COVID-19 (China) according to criteria graph out of Figure 6 . Table 1 .

According to the results, in total, 1,562,500 ARDL model specifications were considered for each of the four cases given the information related to COVID-19 in China. The top 20 results are presented in the criteria graph.

Further, Table 5 summarizes the selected lags for the model Romania and COVID-19 (China) according to criteria graph out of Figure 6 . Figure 7 shows the results of criteria graph for the ARDL model that takes into account the number of new cases and new deaths in Italy, both for the BET stock index return and for the Romanian Government bond (10Y). Likewise, in case of Italy, in total, 1,562,500 ARDL model specifications were considered for each of the four cases. Table 1 . Table 6 exhibits the selected lags for the model Romania and COVID-19 (Italy) in line with criteria graph out of Figure 7 . The results reported in Tables 7 and 8 provides the ARDL bound test for cointegration. If the F-statistic is greater than the upper bound, then the variables comprised in the model are cointegrated and a long-run relationship befall. With reference to new cases in China models (see Table 7 ), the F-statistic for BET_R (18.06988) and RO_BOND (4.523219) models is greater than the upper bound of bounds value at 5%, which is suggesting that long-run relationship occur between the variables. The same result is achieved in the case of new deaths in China models, where the value of the F-Statistic is greater than the upper bound critical value. Hence, the null hypothesis is rejected, meaning that the variables in the model are cointegrated. Figure 7 . The results reported in Tables 7 and 8 provides the ARDL bound test for cointegration. If the Fstatistic is greater than the upper bound, then the variables comprised in the model are cointegrated and a long-run relationship befall. With reference to new cases in China models (see Table 7 ), the Fstatistic for BET_R (18.06988) and RO_BOND (4.523219) models is greater than the upper bound of bounds value at 5%, which is suggesting that long-run relationship occur between the variables. The same result is achieved in the case of new deaths in China models, where the value of the F-Statistic is greater than the upper bound critical value. Hence, the null hypothesis is rejected, meaning that the variables in the model are cointegrated. Table 1 .

Regarding Italy, in all four estimated ARDL models the existence of cointegration is confirmed (see Table 8 ) since the F-statistic is significantly higher than the critical values in I(0) and I(1). Consequently, the examined variables are cointegrated and will move together in long-run.

Further, we will analyze the results of the long-term linkages between selected measures. Table 9 shows the outcomes regarding the long-run causal connections among variables for the model Romania and COVID-19 (China)-new cases. The short-run estimates of ARDL approach are presented in Table S1 . In the first model, the number of new infection cases from China have no effect on the BET index return. However, a decrease of crude oil price leads to a higher uncertainty, consistent with Salisu, Ebuh and Usman [23] , suggesting the necessity for policymakers to diminish fears in financial markets. In addition, the exchange rate negatively influences stock market return in the long-run. The Philadelphia Gold/Silver Index coefficient is positive and significant at the 5% level of significance. Hence, the coefficient of XAU_R indicates that an increase of one unit in Philadelphia Gold/Silver Index leads to over 0.2983 units increase in BET index return in the long-run. The error correction term or adjustment speed provides evidence regarding the rate of convergence to equilibrium, being highly statistically significant. The adjustment speed of −1.017783 shows that deviations from the long-term equilibrium in BET index return are corrected the following day by approximately 101.7783 percent. Regarding the second model from Table 9 , similar to the first model, the new infection cases from China does not influence Romania 10-year bond yield in the long-run. Unlike the previous model, the RO_BOND is negatively affected by XAU_R and indicates that an increase of one unit in Philadelphia Gold/Silver Index leads to over 0.3718 units decrease in RO_BOND return in the long-term. Besides, in the long-run, the return of stock market index SSE 100 negatively influences Romania 10-year bond yield. The coefficient of the error correction term is highly statistically significant. Hence, the Romanian 10-year bond will reach equilibrium with a speed of 185.3068 percent in next day. As well, the short-run results strengthen the lack of impact regarding new infection cases of COVID-19 from China on RO_BOND. Table 10 reveals the outcomes of the long-term connection amongst variables for the model Romania and COVID-19 (China)-new deaths. The short-run results are shown in Table S2 . The empirical findings reveal that the impact is stronger in this case as compared to the model that depends on the number of new cases in China due to COVID-19 (see Table 9 ). However, both models shows that the number of new deaths in China due to COVID-19 has no influence on the BET index return, respectively, on the Romania 10-year bond yield, neither in the short-term, nor in the long-term. Therefore, both research hypotheses are rejected for Chinese COVID-19 figures, similar Topcu and Gulal [86] which established that emerging European countries experienced the lowest influence of the outbreak.

Tables 11 and 12 reveals the results of serial correlation and heteroscedasticity tests for the models Romania and COVID-19 (China)-new cases and Romania and COVID-19 (China)-new deaths. The results support that the models are free from autocorrelation and heteroscedasticity. In the case of models that take into account the effects of new cases and new deaths in Italy, unique relationships are identified between the selected variables, as opposed to the models that explored the impact of coronavirus from China. Table 13 exhibits the outcomes of the long-term causal associations between variables for the model Romania and COVID-19 (Italy)-new cases. The short-run outcomes are exhibited in Table S3 . In the long-run, the results of the first model show the lack of any effect from the number of new cases of COVID-19 in Italy on BET index return. In contrast, the return of Milan stock market index FTSE MIB has a positive long-term impact on the BET index return. As well, the short-run results reveal no impact of new infection cases of COVID-19 from Italy on the BET index return. In contrast to COVID-19 figures from China, in case of Italian new cases of coronavirus, the first hypothesis is still rejected, but the second hypothesis is confirmed.

Moreover, in the second model, several statistically significant relationships are identified. There is found a positive impact of the number of new cases in Italy on the Romania 10-year bond yield in the long-term. In addition, a natural gas futures contract has a positive effect on RO_BOND, while the WTI Oil and Philadelphia Gold/Silver Index has a negative impact in the long-run. Another outstanding outcome is that new infection cases of COVID-19 from Italy negatively influence RO_BOND in the short-run, consistent with Sène, Mbengue and Allaya [36] . Therefore, the related uncertainty triggered by the health emergency may determine investors to get rid of their securities. Table 14 exposes the findings towards long-run linkages between variables for models related to Romania and COVID-19 (Italy)-new deaths. The results of short-run estimates are presented in Table S4 . The first model out of Table 14 exhibits that the number of new deaths from Italy have no effect on the BET index return in the long-run. The Philadelphia Gold/Silver Index coefficient is positive and significant at the 5% level of significance. Hence, the coefficient value of XAU_R indicates that an increase of one unit in Philadelphia Gold/Silver Index leads to over 0.1574 units increase in BET index return in the long-term. However, the short-run results show a negative impact of new deaths cases of COVID-19 from Italy on the BET index return, in line with Okorie and Lin [58] which underlined a transitory contagion effect in the stock markets due to novel coronavirus. In addition, Erdem [55] claimed that the index returns decline and volatilities rise due to corona crisis. Hence, the first hypothesis is confirmed.

The second model shows a negative effect of the new deaths' cases from Italy on the Romania 10-year bond yield in the long-run. In addition, the Philadelphia Gold/Silver Index and the OK crude oil future contract negatively influence RO_BOND in the long-term. Besides, in the long-run, the returns of the stock market index FTSE MIB has no impact on the 10-year Romanian bond. Nevertheless, in the short-run, results show a negative impact of new deaths cases of COVID-19 from Italy on the RO_BOND. Therefore, the second hypothesis is established.

Tables 15 and 16 exhibit the outcomes of Breusch-Godfrey Serial correlation LM test and Breusch-Pagan-Godfrey heteroscedasticity test for the models Romania and COVID-19 (Italy)-new cases and Romania and COVID-19 (Italy)-new deaths. Hence, the models are not threatened by autocorrelation and heteroscedasticity.

With the purpose of exploring the causality between included variables, the Granger causality test is employed. In order to be able to apply the Granger causality test, the data series must be stationary and therefore they were turned it into stationary series. Table 17 displays the results of Granger causality test for the stock market returns and COVID-19 measures. There were identified some bidirectional causal relations between BET_R and FTMIB_R (1st lag), as well as among BET_R and IBEX35_R (1st lag). Besides, some unidirectional causal relations arise from FTSE_R (1st lag), DJIA_R (1st lag and 3rd lag), SSE100_R (1st lag, 2nd lag, and 3rd lag), and XAU_R (1st lag, 2nd lag, and 3rd lag) to BET_R. Nevertheless, no relationship was found between BET_R and the COVID-19 variables. Table 18 shows the outcomes of causalities for the variables concerning commodities, currencies, governmental bonds, and COVID-19. The causalities for the whole world stock indexes, commodities, currencies, and COVID-19 variables are reported in Table S5 . Some bidirectional relationships were found merely for the 1st lag between the 10-year Romanian bond and few stock market indices returns, namely CAC40, DAX, and IBEX 35. Besides, unidirectional relationships for 1st lag, 2nd lag, and 3rd lag occurred from returns of DJIA, S&P 500, FTSE 100, FTSE MIB, SSE 100, and the number of new cases in Italy due to COVID-19 to the 10-year Romanian bond. 

One of the most severe stock market crashes was registered in March 2020 [79] due to the occurrence of the novel coronavirus COVID-19 pandemic [55] . The research contributions are twofold. First, we investigated whether the Romanian stock market is affected by the COVID-19 pandemic outbreak. Second, our paper explored the causalities among COVID-19 and major stock market returns, as well as between pandemic measures and several commodities. In this regard, we used daily stock market returns over the period December 31, 2019-April 20, 2020 for the following economies: USA, Spain, Italy, France, Germany, UK, China, and Romania. We have selected a wide range of variables that allow us to achieve our goal, such as stock market indices, new number of cases of illness, new number of deaths in China and Italy, exchange rate, commodity indices, Romanian bonds. As far as we know, this is the first study addressing the impact of the COVID-19 from both China and Italy crisis on the Romanian capital market and the 10-year Romanian bond.

After examining the stationarity of the selected data series and due to the mixed results, we conclude that the ARDL model is the most appropriate to explore the short-term and long-term causal associations among Romanian stock market and novel coronavirus. In the case of the model that includes the number of new deaths in China due to COVID-19, it is found that the impact of the coefficients is stronger compared to the model that depends on the number of new cases in China due to COVID-19. At the level of these two models, no effect was identified from the number of new deaths in China due to COVID-19 on the BET index return, respectively on the Romania 10-year bond yield, neither in the short-term, nor in the long-term.

With reference to the model that cover the new cases of coronavirus from Italy, short-run results provide support for a negative impact of new Italian COVID-19 cases on the Romania 10-year bond yield. Taking into account the number of new deaths in Italy we found that it has no effect on the BET index in the long-term, but the short-run results exposes a negative effect. Besides, the ARDL models showed a negative effect of the new deaths' cases from Italy on the Romania 10-year bond yield both in the long-run and short-run.

Granger causality test exhibits bidirectional causal relations between returns of BET and FTSE MIB, IBEX, as well as a unidirectional causal relation from FTSE 100, DJIA, SSE 100, and Philadelphia Gold/Silver Index to BET index return. However, no relationship was found between the BET index return and the COVID-19 variables. Some bidirectional relationships were found between the 10-year Romanian bond and a few stock market indices (CAC 40, DAX, and IBEX 35). Unidirectional relationships occurred from returns of DJIA, S&P 500, FTSE 100, FTSE MIB, SSE100, and the number of new cases in Italy due to COVID-19 to the 10-year Romanian bond. Therefore, the empirical findings from ARDL model and Granger causality test confirmed both the presence of a long-term and short-term relationship between Romanian capital market and COVID-19 variables. The findings show that the Chinese COVID-19 numbers have no impact on the Romanian financial market. In addition, it was found that the 10-year Romanian bond is more sensitive to the news related to COVID-19 than the index of the Bucharest Stock Exchange, similar to Pavlyshenko [39] , Mamaysky [88] .

The paper may have some policy implications. As long as the BET index is not influenced by COVID-19 variables, this may suggest evidence of an inefficient market, in line with Beck, Flynn and Homanen [52] , Mensi, Sensoy, Vo and Kang [72] . There are required policies to increase market efficiency though longstanding and sustainable growth rather than administering short-term interest rates [73] . The investors should seek long-term horizons of investing since the monetary and fiscal policies set by governments will alleviate the harmful effects of COVID-19. The policymakers should be aware that corona crisis may be an occasion to improve the discrepancy among Romania and developed nations of European Union. In this regard, a substantial share of the budget should be expended to alleviate this pandemic [59] . A suitable clinical stream is vital so as to ensure a reliable supervision of patients [92] . Rearrangement of public expenditure to enlarge the absorptive volume of healthcare organizations is essential [46] . Therefore, public health expenditures should be increased, along with offering direct income funding to exposed populations via cash transfers, support to affected manufacturing areas and corporations through transient tax cuts, deferral on debt reimbursements, and interim credit lines [3] .

Supplementary Materials: The following are available online at http://www.mdpi.com/1660-4601/17/18/6729/s1, Table S1 : ARDL short-run coefficient estimates for the model Romania and COVID-19 (China)-new cases, Table S2 : ARDL short-run coefficient estimates for the model Romania and COVID-19 (China)-new deaths, Table S3 : ARDL short-run coefficient estimates for the model Romania and COVID-19 (Italy)-new cases, Table S4 : ARDL short-run coefficient estimates for the model Romania and COVID-19 (Italy)-new deaths, Table S5 : The results of the Granger causality test for world stock indexes, commodities, currencies and COVID-19 variables. 

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This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license

Funding: This research received no external funding.

The authors declare no conflict of interest.