Comovement of Fear Index,Stock Returns,Brent Oil Prices in BRIC Countries: The Case of COVID-19

Abstract

The study investigates long-run and short-run cointegrating relationship between stock market returns, fear index (VIX), brent crude oil prices and growth in deaths due to the COVID-19 pandemic for BRIC countries using daily data from 23 January 2020 to 24 August 2020 using Autoregressive Distributed Lag (ARDL) model. CUSUM test and serial correlation test estimates point towards the robustness of the model used. The evidence reveals that for India and Brazil, with the outbreak of COVID-19, decrease in crude oil prices and increase in volatility index, the stock returns started declining in the short run, but the impact has declined and the stock returns have regained in the long run. For China, due to the outbreak of COVID-19 and increase in fear index, stock returns declined in the short run, but the Chinese economy has recovered well due to a strong industrial and services sector. For Russia, increase in deaths due to COVID and decline in oil prices has impacted the stock returns in the long- and short run. Due to a decline of 53% for crude oil prices from January 2020 to May 2020, the Russian economy would face the consequences in the long run as well. The results suggest that though BRIC countries were impacted by growth in COVID-19 deaths, but the recovery trajectory and stability has resumed for all countries except Russia. Results of Granger Causality indicate a bidirectional causality between VIX and stock returns for the Indian market.

Keywords

Stock returns Brent crude oil prices VIX COVID-19 ARDL Granger causality

I. Introduction

On December 2019 in Wuhan, the capital city of Hubei province of China, the first COVID case was identified (Sohrabi et al., 2020; Yang et al., 2020). On 30 January 2020, the World Health Organization (WHO) declared COVID-19 as a health emergency of international concern. The virus has caused 848,557 deaths worldwide, with 25,291,896 cases reported till August 2020. The pandemic has had clear economic and financial impacts, with business failures and increased unemployment. The Shanghai stock market fell by 8% in the first week of February, with the shock spreading to other international markets. GDP growth is expected to decline by 2% in the United States and Japan and by 3% in the EU (Gormsen & Koijen, 2020).

BRIC countries are protagonists as the most promising large emerging economies in the multipolar world, covering around 25% of the global GDP. Simultaneously, they have also been attracting the most relevant investments. The progressive relevance of these countries embeds them to be placed as world leaders in the global economy. Needless to say, in the unconventional and unexpected times such as the COVID-19 pandemic, these nations deserve special attention. Critical to this discussion is testing if there is presence of any long-run relationship among stock market returns, increase in deaths due to COVID-19 and financial markets.

To gauge the market’s expectation of 30ay volatility and help the investors know about the current fear and stress that exists for the market, a CBOE volatility index (VIX) is used. It is popularly known as an ‘investor fear gauge’ (Aharon & Qadan 2017; Akdag 2019; Cheuathonghua et al., 2019; Cinar & Uzmay, 2017; İskenderoglu & Akdag, 2020; Whaley, 2000; Woodley et al. 2019). Uncertainty has a negative effect on stock returns (Bash & Alsaifi, 2019). While multiple studies existed for employing of VIX as a fear quotient for developed countries, its use for emerging countries was doubtful. A study by Sarwar (2012) convincingly assessed the legitimacy of using VIX to gauge investor fear for BRIC countries. A strong empirical evidence to support its usage for BRIC countries was found.

The pandemic led to social lockdown and travel restrictions, causing oil demand collapse. Brent crude oil price dropped from $66 a barrel in December end of 2019 to $45 a barrel in March 2020. Due to the lack of consensus between the Organization of the Petroleum Exporting Countries (OPEC) and its allies, the oil price fell to $34 a barrel. Brent oil prices signal towards the financialisation of commodity markets (Ajmi et al., 2014) and can be utilised as a factor to test the relationship between equity markets and commodity markets in BRIC nations. In this context, Ciner (2001) forayed into testing whether a relationship exists between real stock returns and oil price futures and found significant results. A rational response between stock markets and oil price changes also exists in the Norwegian market (Gjerde & Saettem, 1999).

Figure 1.

Source: Authors’ own compilation.

Specifically, crude oil is the most universal commodity in the sense that fluctuations in its price are strongly correlated with fluctuations in equity markets. China, due to its strong industrial presence and high population, has shown a sharp incline in consumption of oil (Figure 1) over the years, whereas for India, Brazil and Russia, the consumption has been relatively stable. Oil price fluctuations interest the researchers not only because they directly impact the economic activity but also because fluctuations in prices of oil might even affect international stability (Leigh et al., 2003).

The stock markets also responded negatively to the growth of COVID-19 cases (Ashraf, 2020). Due to the decrease in the Shanghai stock market index, the central government injected $174 billion of liquidity in the market. In July 2020, even after the pandemic, the Chinese equity market saw an investment of $7.6 billion. This could be due to the hope and uncertainty of the Chinese market during this crisis. For China, positive news improves the stock market and trading of stocks, while negative news hinders it (Li & Wei, 2018). Estimates provide ample evidence of similarities in the correlation dynamics between the crude oil and stock volatility series (Ding et al., 2017). Going beyond previous research, Ajmi et al. (2014) assess the impact of Islamic stock market indices to global risk factors (VIX) and finance systems (international crude oil market).

Investigating the relationship between equity markets returns, risk factor (VIX) and oil prices of BRIC nations in a pandemic that hampers industrial activity has several potential benefits. First, it explores the less-explored characteristics of asset markets of emerging countries, which are otherwise well-documented for developed countries. Second, it adds to valuable research using a comprehensive dataset, which may be of interest to market participants, investors and regulators. With the onset of uncertainty, such as COVID-19, many research questions arise. Are the stock returns impacted by Brent crude oil prices that have fluctuated due to COVID pandemic? Are the returns affected by growth in deaths due to COVID? The study examines if there is presence of long-run and short-run relationship between fear index, stock returns, oil prices and growth in deaths due to COVID in BRIC countries. The article adds to literature as a special case, addressing testing the long-run and short-run interrelationship during the COVID-19 pandemic, and introduces growth in deaths due to COVID as another potential variable that might affect equity returns of BRIC countries.

Overall, the motivation of the research lies in the extent of fluctuations in the equity markets, which primarily exhibit higher interrelations with other markets as well as sentiments of market participants. The paper intends to test the interrelations between VIX, brent oil prices and growth in deaths due to Covid-19 and the stock returns.

Figure 2.

Source: Authors’ own compilation.

Figure 3.

Source: Authors’ own compilation.

Figure 2 shows the fall in the crude oil prices during the initial months of the year, which started stabilising by second quarter of 2020. Figures 3 and 4 depicts the change or growth in deaths due to COVID in China and other BRIC countries. There has been a steady increase in the death rate, which stabilised post June 2020.

Figure 4.

Source: Authors’ own compilation.

The remaining paper is as follows: Section II discusses the review of literature, Section III describes the methodology, data and variables used in the study. Section IV discusses the empirical analysis. Section V presents the discussion of the results and Section VI concludes the article.

II. Review of Literature

COVID-19 has had a considerable impact on financial markets and institutions, which could be direct or indirect (Goodell, 2020). With an increase in deaths and confirmed cases of COVID-19, the US markets became more illiquid and volatile (Baig et al., 2020). Extremely large disasters have negative impacts on the outputs in the long and short run (Cavallo et al., 2013). An event like a terrorist attack effects the commodities, stocks and bonds in a negative way (Chesney et al., 2011). Pandemics bring a large loss in economic conditions of the country as well. Expected value of losses for national income varies from 0.3% for high income countries to around 2% for low middle level economies. Stock market volatility has been affected more by COVID-19 compared to other diseases like Ebola (Baker et al., 2020a, Schell et al., 2020). Ebola virus in the past brought great human and financial losses (Ross et al., 2015). Diseases like SARS, which spread rapidly from one country to another, brought a disproportionate economic impact than a health impact (Smith, 2006). Spanish Flu led to an economic decline in GDP and consumption to around 6–8% globally (Barro et al., 2020). In the United States, due to the current pandemic and business closures, there has been a loss of around $700 billion, but it saved 36,000 lives (Barrot et al., 2020). Also, the expected partisan gains were also less during COVID times for the United States (Apergis & Apergis, 2020).

Even though the virus broke out in China, the Chinese economy is in a much better position than other developing economies (Liu et al., 2020). Neither gold nor cryptocurrencies have significantly affected the stock markets in China (Corbet et al., 2020). So, though the Chinese market stabilised itself, the global market got hit during the later part of the virus spread (Ali et al., 2020). In the United States and the United Kingdom, the implied VIX rose in February, peaked in March and fell back by March end as the stock markets improved (Altig et al., 2020). VIX as an investor fear gauge index has negatively impacted the stock returns for emerging countries (Sarwar et al., 2020). High sentiment market outperforms low sentiment markets (Sun et al., 2019; Zaremba et al., 2020).

Major global and political events have impacted the oil price, and positive and negative correlations exists between the oil price and the fear index (Qiang et al., 2019). When the global activity is weak, there is a huge, unexpected rise in the oil prices (Ratti & Vespignani, 2013). Prior to the global financial crisis, there was a huge rise in the demand for oil due to disruption in the supply of oil as well as a global increase in demand for oil. Post the crisis, due to the decrease in US dollars, the oil prices crashed (Bhar & Malliaris, 2011). For the United States, the oil market became inefficient post the crisis (Gil-Alana & Monge, 2020). Volatility of crude oil improves the directional predictability of the VIX, especially when the oil volatility is high. Oil has been found to be the best hedge for emerging stock market prices (Basher & Sadorsky, 2016). Global oil and the US energy stock market’s VIX are interlinked with a long-run cointegration relationship (Dutta, 2018; Qiang et al., 2019). Implied volatility for crude oil price markets contains important information for predicting future volatility as well (Bakanova, 2010). The risk perception of the investors globally impacts the gold prices, with a long-run relationship between gold, silver and exchange rate (Sari et al., 2011). Studies conducted in developing markets like India showed a positive impact of crude oil on the Indian VIX and a negative impact of gold (Mehta and David, 2019). BRICS index returns commoved with oil prices, especially during the global financial crisis period. Gold does not comove with stock index, proving to be a hedge for countries against extreme market movements (Mensi et al., 2018). Also, during times of financial markets volatility, there is correlation between crude oil prices and stock market returns in China (Nadal et al., 2017). For Russia, external shocks such as VIX, price of brent crude oil have significantly affected the dynamics of macro-economic indicators (Shevelev, 2017). Oil fluctuations have affected the financial markets and the currencies. Lowering of oil price boosts the UK pound but hurts several sectors that are benefitted with oil supplies such as industrials and transportation (Atil et al., 2014). With the onset of COVID-19, firms that were exposed to China underperformed. Post the spread to the United States and Europe, corporate debt market emerged as a valuable driver. The effects of a health crisis were increased through the financial claims (Ramelli & Wagner, 2020).

Studies on Relationship Between the Volatility Index, Stock Market Returns and Brent Crude Oil Prices Before COVID-19 and During COVID-19

Post COVID, the results depicted that for India and Brazil, there is no long-run association between the stock returns, the VIX, brent crude oil prices and growth in deaths. For China and Russia, the fear index impacts the stock market return in a negative way. Before the COVID breakdown, a negative relationship has been found between the Indian VIX and stock market return (Chakrabarti & Kumar, 2020). For Brazil, investors could anticipate instabilities in the Brazilian market by getting indications from the fear index and could identify opportunities to enter and exit the markets (Cainelli et al., 2020). Though stock market returns and VIX move inversely, China observed them moving in tandem before the COVID crisis (Liu, 2019). Unlike the Chinese and Brazilian non-contemporaneous significant correlations between VIX and stock returns, none of the non-contemporaneous cross-correlations between changes in VIX and equity returns for India and Russia are significant (Sarwar, 2012). Before COVID, empirical evidence of time varying differential dependence between Indian stock market returns and brent crude oil have been found (Singhal & Ghosh, 2016). During COVID, for the Indian market, the variables hold a significant relationship in the short run. This implies that the stock returns are impacted by the oil price, fear index and growth in deaths due to COVID in the short run but not in the long run. Pre COVID, the corelation between crude oil and the Indian stock market returns has been found to be higher during the 2008–2014 period (Jain & Biswal, 2016). A positive causal effect from crude oil price changes to Asian market stock returns has been found (Yousaf & Hassan, 2019). Pre COVID, asymptotic dependence between Chinese stock market and crude oil prices have been concluded (Chen & Lv, 2015; Huang et al., 2015; Li & Wei, 2018). Before the COVID time period, Brazilian market has been found to be sensitive to the variations in oil prices (Teixeira et al., 2020; Nava et al., 2018). For Brazil, the brent crude oil increase and growth in deaths due to COVID negatively impact the stock returns. During COVID, the study finds that for Russia, the increase in brent crude oil prices has a negative impact on the stock returns. Pre COVID, brent crude oil price has the strongest transmission eject on the Russian stock market return (Zivkov et al., 2018).

III. Data and Methodology

The study aims to find out the long-run and short-run relationship between VIX, stock market returns, growth in deaths due to COVID-19 (DR) and Brent crude oil spot prices (BO) for the BRIC countries. Investors cannot buy VIX directly, so they take positions in it through the future contracts/options. We use daily data extracted from websites of respective indices from 23 January 2020 to 24 August 2020 for this study (Table 1).

Table 1.

Description of Various Indicators Used in the Study.

Indicator	Description	Source
Stock returns	Brazil: -BOVESPA China: HangSang Index Russia: Moscow exchange India: National Stock Exchange	Websites of respective indexes
Crude oil (Brent)	Spot price of Brent crude oil for BRIC countries	tradingeconomics.com
VIX	Volatility index (VIX) also known as fear index China ETF volatility index India Nifty VIX Index Russia VIX CBOE Brazil ETF volatility index (VXEWZ)	India: https://www1.nseindia.comRussian VIX Index - Investing.comBrazil: https://in.investing.com/www.cboe.com
Growth in deaths	Calculated as growth in deaths due to COVID-19 based on WHO data	WHO data

Source: Authors’ own compilation.

The time period was chosen because COVID-19 early confirmed cases and deaths began in this time period, with growth in March and stability in August 2020. The government response curves also started declining and flattening post April 2020 (Hale et al., 2020). The data for China VIX India VIX, Russia VIX and Brazil VIX has been taken from Cboe website. Brent crude spot oil price (dollar per barrel) is taken from www.investing.com. Daily growth in death cases for China has been extracted from Worldometer. The hypothesis to test is as follows:

H_1a: Is there presence of long run cointegrating relationship between the VIX, stock market returns, Brent crude oil prices and growth in deaths due to COVID-19 in BRIC countries?

H_1b: Is there presence of short run relationship between the China VIX, stock market returns, Brent crude oil prices and growth in deaths due to COVID-19 in BRIC countries?

H_1c: Is there existence of short run causal relationship from VIX, Death growth rate and Brent crude oil prices to stock market returns for BRIC countries?

In applied econometrics, several studies by Engle and Granger (1987), the Granger (1981), Autoregressive Distributed Lag (ARDL) cointegration technique, Johansen and Juselius (1990), bound test of cointegration by Pesaran et al. (1999) and Pesaran et al. (2001) have been used to establish long-run relationship among the variables and reparametrising them to the Error Correction (ECM). The present study makes use of ARDL cointegration technique to establish the long relationship among the variables.

The hypotheses are tested by the ARDL model developed by Pesaran et al. (1999) to study the long-run and short-run relationship among the variables. Cointegration is a method to study long-run relationship among VIX, stock market returns, growth in deaths due to COVID-19 and brent crude oil prices. The advantage of the method is that it overcomes endogeneity problems, computes long-run and short-run relationship together and can be estimated if the regressors are I(0) or I(1) (Pesaran & Shin, 1996). The series should not be I(2), otherwise the F-statistics computed by the model becomes invalid. So, before applying ARDL model, it is verified that none of the variables should be I(2). To check the stationarity of the variables, the study uses Augmented Dicky Fuller Test (ADF). Akaike Information Criteria (AIC) and Schwarz Information Criteria (SIC) are used to select the number of lags in the model. The null hypothesis states that there is no long-run relationship among the variables. If computed F-statistic value is greater than the upper bound, the null hypothesis is rejected, and if it is below the lower bound, the null hypothesis cannot be rejected. If it is between the lower and upper bounds, the results are inconclusive (Narayan, 2005).

The long run model is as follows:

SEt = β 1 + β 2 VIXt + β 3 BOt + β 4 GDt + ε t

(1)

To check presence of long-run cointegration, the following equation is run.

S E t = β_{1} + \sum_{i = 1}^{P_{1}} β_{2} S R_{t - i} + \sum_{i = 0}^{P_{2}} β_{3} Δ V I X_{t - i} + \sum_{i = 0}^{P_{3}} β_{4} Δ D R_{t - i} + \sum_{i = 0}^{P_{4}} β_{5} Δ B O_{t - i} + \emptyset_{1} S R_{t - i} + \emptyset_{2} V I X_{t - i} + \emptyset_{3} D R_{t - i} + \emptyset_{4} B O_{t - i} + E_{t - i}

(2)

∇ = First Difference Operator

$β_{1}$ = Constant Term

$β_{2} - β_{5}$ = Short Run Coefficients

$\emptyset_{1} - \emptyset_{4}$ = Long Run Coefficients

$P_{1} - P_{4}$ = Lag Length

$E_{t}$ = White Noise Error Term

$E C_{t}$ = Error Correction Term

Once we are able to establish the long run relation with the help of the F-statistic, we can estimate the ECM.

The ECM representation is as follows:

Δ S R_{t} = \propto_{1} + \sum_{i = 1}^{P_{1}} \propto_{2} S R_{t - i} + \sum_{i = 0}^{P_{2}} \propto_{3} Δ V I X_{t - i} + \sum_{i = 0}^{P_{3}} \propto_{4} Δ D R_{t - i} + \sum_{i = 0}^{P_{4}} \propto_{5} Δ B O_{t - i} + E C t_{t - i}

(3)

The aim of ECM is to show the speed of adjustment to equilibrium after a shock in the short run. Diagnostic tests such as Bresuch Pagan Godfrey Heteroskedasticity and Bresuch Godfrey serial correlation tests is used. To explore the stability of the coefficients, cumulative sum of recursive residuals (CUSUM) is used. If the plot lies within the bound at 5% level of significance, the coefficients in the regression model are said to be stable (Pesaran et al., 2001).

IV. Empirical Analysis and Results

Table 2.

Descriptive Statistics.

		SR	VIX	OIL	GD
China	Mean	−0.00015	32.68348	34.19299	0.04536
China	Std. Dev	0.017856	9.477506	10.28922	0.072396
India	Mean	0.000977	34.59727	35.27511	0.065072
India	Std. Dev	0.025688	14.74761	10.19399	0.113525
Russia	Mean	0.000369	38.59374	36.33512	0.14155
Russia	Std. Dev	0.014854	12.01642	9.795827	0.568183
Brazil	Mean	0.226176	58.99137	32.84805	0.095907
Brazil	Std. Dev	2.023921	17.38775	11.6821	0.518343

Source: Authors’ own computation.

The descriptive statistics (Table 2) point out interesting figures and revelations about the data. Except China, no other country experiences negative returns in the stock market index. The ratio for growth in deaths is surprisingly highest for Russia and lowest for China in the sample period. All BRIC countries also recorded high volatility in VIX, also known as fear index, with Brazil having the maximum.

As can be seen in Table 3, the variables in the model are checked for stationarity using ADF test (Harris, 1992). The variables need to be stationary to avoid any spurious regression. If the time series variables are non-stationary, they cannot be applied in any regression model. The variables for different countries are integrated of the order I(0) or I(1). This is in line with the argument of Kapetanios et al. (2011), that variables for ARDL model should be I(0) or I(1). Due to the presence of mixing of degree of order, the study employs an ARDL-bound testing approach to check for the presence of long-run and cointegrating relationship among the variables.

Table 3.

Unit Root Analysis, Stock Returns, Brent Oil and Growth in Deaths.

SR(ADF)			VIX(ADF)		BO(ADF)		GD(ADF)
	Level	First Difference	Level	First Difference	Level	First Difference	Level	First Difference
China	0.000***	NA	0.000***	NA	0.816	0.000***	0.378	0.000***
India	0.000***	NA	0.705	0.000***	0.812	0.000***	0.238	0.000***
Russia	0.000***	NA	0.000***	NA	0.781	0.000***	0.000***	NA
Brazil	0.000***	NA	0.000***	NA	0.040*	NA	0.000***	NA

Source: Authors’ own computation.

Note: *, ** and *** denote significance at 10%, 5% and 1% level, respectively.

Post the stationarity analysis, the study proceeds to compute the appropriate lag order at which AIC and SIC would be the least (Hurvich & Tsai, 1993). The lag order by AIC provides a more robust and reliable information compared to the other methods. Having an appropriate lag order is an important criterion for an ARDL model. It is also a key factor to avoid the presence of serial correlation among the variables. At lag 3, the value of AIC was found to be the minimum for all the countries. The model is run with constant and no trend. Equation (1) is estimated using ordinary least square (OLS) method, and F-statistic is computed along with Pesaran critical value to determine existence of long run relationship.

Table 4.

Results of ARDL-bound Rest: Long-run Relationship.

	Computed F-statistic Value	5% Critical Bounds		Conclusion
		I(0)	I(1)
India	8.468	2.39	3.38	Conclusive
Russia	7.639	2.39	3.38	Conclusive
China	14.106	2.39	3.38	Conclusive
Brazil	10.305	2.39	3.38	Conclusive

Source: Pesaran et al. (2001)

Table 4 indicates the ARDL approach to test for the long-run relationship among the variables. The results for F-statistics shows that calculated F-statistics for all BRIC countries is higher than the upper bound level and, therefore, a long-run relationship exists among the variables for all the countries. It suggests that there is a long-run cointegrating association among the variables. Having established cointegration, the study proceeds to estimate the long-run and short-run estimates of the ARDL model.

Table 5.

Results of Long-run Estimates.

	India		China		Russia		Brazil
Variables	Coefficient	Probability	Coefficient	Probability	Coefficient	Probability	Coefficient	Probability
C	0.029069	0.142	0.024063	0.047**	0.06334	0.000***	0.003241	0.861
VIX	−0.000387	0.142	−0.000473	0.063*	−0.000837	0.000***	−4.99E-05	0.766
BO	−0.000381	0.279	−0.000225	0.254	−0.00083	0.000***	5.45E-05	0.849
GD	−0.018991	0.434	−0.023822	0.456	0.00026	0.917	0.000285	0.943

Source: Authors’ own computation.

Note: *, ** and *** denote significance at 10%, 5% and 1% level, respectively.

Table 5 shows that all the long-run estimates are significant to explain the dependent variable. For India and Brazil, there is no long-run association between the stock returns, the VIX, brent crude oil prices and growth in deaths. For China and Russia, the fear index impacts the stock market return in a negative way. This is in line with the findings of other studies (Altig et al., 2020; Baker et al., 2020b; Onali, 2020; Salisu & Akanni, 2020). COVID-19 spillover effects have led to risk aversion in stock markets and increased volatility (Liu et al., 2020; Papadamou et al., 2020). For Russia, the increase in brent crude oil prices has a negative impact on the stock returns. The negative relationship between stock returns and brent crude oil prices supports the findings of other studies (Albulescu, 2020; Lahmiri & Bekiros, 2020; Mzoughi et al., 2020; Sari & Kartal., 2020), indicating that an increase in oil prices has a negative impact on the implied volatility for the next 30 days.

Table 6.

Results of Short-run Estimates of ARDL Model.

	China		India		Russia		Brazil
Name of Variable	Coefficient	Probability	Coefficient	Probability	Coefficient	Probability	Coefficient	Probability
C	−6.08E-04	0.708	−0.00059	0.731	0.00112	0.359	−0.0014	0.994
ΔSR	−0.02853	0.774	−0.20451	0.067*	0.122972	0.191	0.011066	0.909
Δ VIX	−0.00193	0.001***	−0.00548	0.000***	0.000629	0.144	0.017984	0.745
Δ BO	−6.36E-05	0.942	0.002509	0.006***	−0.00252	0.000***	−0.03607	0.094*
ΔGD	0.051196	0.049**	−0.07642	0.002***	0.005542	0.000***	−0.88023	0.001***
ECT(-1)	−1.27512	0.000***	−0.83533	0.000***	−0.80452	0.000***	−1.16009	0.000***

Source: Authors’ own computation.

Note: *,** and *** denote significance at 10%, 5% and 1% level, respectively.

Further, Table 6 presents the short estimates for the model. For China, the fear index is significant in the long- as well as the short run. Therefore, the stock returns are negatively affected by increase in volatility in the short- as well as the long run. For Brazil, the brent crude oil increase and growth in deaths due to COVID negatively impacted the stock returns only in the short run. For India, the variables hold a significant relationship in the short run. It implies that the stock returns are impacted by the oil price, fear index and growth in deaths due to COVID in the short run, but not in the long run. For Russia, the growth in COVID deaths impacted the market returns in short run but not in the long run. The fear index might not impact in the short run but would do so in the long run. The oil price has a negative and significant effect in the long- as well as the short run. The coefficient of the lagged correction term should be negative and significant, indicating long-run equilibrium when the system is subjected to a shock. If the error correction term (ECT) value lies between −1 and −2, it implies existence of dampened fluctuations while moving from fear index, oil prices and growth in deaths to the stock returns among the equilibrium path (Narayan & Smyth, 2005). Therefore, for China and Brazil, instead of converging to the equilibrium path directly, the process of error correction fluctuates itself along the long-run value in a diminishing manner. For India, the system converges to its equilibrium value at a speed of 83%, and for Russia, the convergence would be at the speed of 80% whenever the system is exposed to a shock. Granger causality test is used to measure the short-term causality among the variables.

Table 7.

Granger Causality Results.

	India
	SR		VIX		BO		GD
	Coefficient	Probability	Coefficient	Probability	Coefficient	Probability	Coefficient	Probability
SR	–	–	5.417577	0.019****	0.397937	0.528	14.70671	0.000***
VIX	52.52402	0.000**	–	–	0.494842	0.481	1.721067	0.189
BO	17.55699	0.000***	9.973397	0.001***	–	–	0.150681	0.697
GD	11.13158	0.003***	0.150909	0.697	0.377395	0.539	–	–
	China
	SR		VIX		BO		GD
	Coefficient	Probability	Coefficient	Probability	Coefficient	Probability	Coefficient	Probability
SR	–	–	0.1817	0.669	0.559764	0.454	11.08924	0.000***
VIX	10.8636	0.001***	–	–	6.950431	0.008***	1.188679	0.275
BO	0.0053	0.941	1.0403	0.307	–	–	0.918926	0.337
GD	3.9614	0.046**	7.8255	0.005***	1.60338	0.205	–	–
	Russia
			VIX		BO		GD
	Coefficient	Probability	Coefficient	Probability	Coefficient	Probability	Coefficient	Probability
SR	–	–	0.0953	0.755	0.0163	0.898	2.9613	0.085
VIX	2.1574	0.141	–	–	0.0001	0.988	1.0347	0.309
BO	13.3745	0.000***	5.1093	0.023**	–	–	0.0245	0.875
GD	10.9256	0.000***	1.9819	0.159	1.3888	0.238	–	–
	Brazil
	SR		VIX		BO		GD
	Coefficient	Probability	Coefficient	Probability	Coefficient	Probability	Coefficient	Probability
SR	–	–	0.1498	0.698	0.2301	0.631	2.8658	0.090*
VIX	0.0130	0.909	–	–	0.3313	0.564	3.2364	0.072*
BO	2.8507	0.091	14.7265	0.000***	–	–	0.1592	0.689
GD	10.7825	0.024	5.0789	0.024**	0.0011	0.972	–	–

Source: Authors’ own computation.

Note: *, ** and *** denote significance at 10%, 5% and 1% level, respectively.

Although cointegrating relationship implies the presence of Granger causality, it does not indicate the direction of causality among the variables. Therefore, Granger causality is applied to check for the direction of causality among the variables (Table 7) For India, there is a bidirectional causality running between VIX and SR. With an increase in the fear index, there is an immediate decrease in the stock market returns (Sarwar & Khan 2017; Sarwar, 2014). An oil price change causes an immediate change in the stock returns (Gjerde & Saettem, 1999). Also, with an increase in deaths due to COVID, an economic uncertainty is created, which leads to a decrease in the stock returns. For China, there is unidirectional causality running from the fear index to the stock returns and from growth in death due to COVID-19 and the fear index. Also, there is a unidirectional causality running from implied VIX to brent crude oil prices (Qiang et al., 2019). For Russia, unidirectional causality has been found from oil prices to the stock returns and fear index. Also, unidirectional causality exists from increase in deaths due to COVID and stock returns. Similar to Russia, unidirectional causality runs from oil prices to stock returns and fear index for Brazil. Unidirectional causality runs from growth in deaths due to COVID to stock returns and fear index.

The study conducts diagnostic and stability tests to check for the robustness of the model.

Table 8.

Serial Correlation Test.

Breusch-Godfrey Serial Correlation LM Test
China
Obs*R-squared	2.516	Prob Chi Square	0.112
Brazil
Obs*R-squared	4.127	Prob Chi Square	0.127
India
Obs*R-squared	4.935	Prob Chi Square	0.084*
Russia
Obs*R-squared	0.094	Prob Chi Square	0.758

Source: Authors’ own computation.

Serial correlation (Table 8) is checked using Breusch Godfrey Serial Correlation Test. The null hypothesis states that there is no serial correlation in the residuals. As the p-value is greater than 5%, the chi-square values indicate there is no serial correlation in the model. The stability of the coefficients in the model is tested using CUSUM test.

The null hypothesis states that the coefficients are stable in the model. The null cannot be rejected, and as for the CUSUM test, the cumulative sum remains with thin the 95% confidence band limit. The CUSUM test proves that the model is not mis-specified and there would be no structural change in the model over the period of time.

V. Discussion of Results

For India, in the short run, with the announcement of lockdown, Sensex dropped by 40% from 42,273 points to 25,638 in January. But in the long run, over the six-month period, the market recovered by 50% to over 38,000 points. The Indian market stabilised, and inflows in the capital market by domestic and foreign investors had started. This is also because investors believe that the government and central bank will back the capital in case of any failure in the market. Capital markets have provided secure returns compared to real investments. Decrease in brent crude oil prices led India to buy crude oil worth $16.7 million, filling its strategic crude oil reserves. Brent crude oil hit a 21-year low during COVID-19 and US oil futures fell to a negative value for the first time in history. Thereby, the decrease in oil price, increase in COVID deaths and volatility impacted the stock market returns in the short run, but the markets have recovered. Even though there has been decrease in economic activity, increase in unemployment, and changing pattern of work, study and travel, the stock markets is performing well. Therefore, in the long run, the stock returns are not affected by VIX, crude oil prices and COVID deaths for India. For Brazil, growth as measured by the economy activity index has reached pre COVID levels. Brazil faced the world’s second most severe outbreak of coronavirus deaths, but due to aggressive fiscal and monetary policies and less strict quarantine measures, the economy has rebounded. As it can be gauged from the results, the stock market declined by 50% with the outbreak of COVID in Brazil. As half of Brazil exports comes from commodities, with the decline in crude oil prices the markets declined sharply in the short run. But with the passage of time, the equity markets have seen domestic investor inflows. The short-run impact on Brazilian markets was due to the fact that China exports oil from Brazil. Therefore, with COVID, increase in China’s demand for oil decreased and impacted the economy. Brazil kept some of its sectors open, with services sector’s opening leading to a revival of the stock market returns by 30%. Therefore, in the long run. the stock market returns were not affected by oil price decline, COVID deaths and the fear index. China, being the market that gave birth to the virus, experienced a surge in the volatility and a decrease in stock market returns. But over time, the Chinese economy became the first economy to grow since the COVID impact. Though there was a decline of 6.8% in GDP during the outbreak of the virus, the GDP has currently increased to 3.2% compared to 2019. This has been due to increase in exports, industrial production and plans by government to invest in infrastructure. Though the VIX impacts the stock returns in the long and the short run, but the volatility has reduced due to the revival of the economy. From the results, it is seen that the Russian stock market returns are being impacted by oil decrease, increase in deaths due to COVID and VIX in the long and short run. Energy exports account for half of Russian exports, which got hit due to price crash and low demand. There has been a loss in the tax revenue that Russia earned from oil exports as the oil price fell below $42 per barrel. Russian economy had stabilised in 2016, but the pandemic increased the VIX and destabilised the moderately growing stock markets. Stock markets fell down by 30% during COVID. The analysis of the BRIC countries depicts that India, China and Brazil are on a path of recovery, but Russia will still take time to stabilise its market and economy.

VI. Conclusion

The study delves into the relationship between the fear gauge index, brent crude oil prices, impact of COVID-19 induced deaths and stock returns for BRIC countries using an ARDL-bound testing approach. VIX represents the feelings and fearfulness of the investors and the degree of protection they want for future investment. Though there is a short-run relationship among variables for India and China, in the long run, stock returns would not be significantly impacted by fear index, oil prices and growth in deaths due to COVID. Even though countries have decided on the policy of ‘make local and buy local’, the Chinese manufacturing market has not been impacted and has shown an increase in exports as well. For India, even though there have been rising COVID cases and weak labour market, the country is back on a growth trajectory and stable stock market returns. Energy exports play an important role in the Russian economy. Due to lockdown and recession, there was a reduction in the demand for oil, leading to loss of revenue from oil exports. The study finds that the BRIC countries would recover from the crisis in the long run.

The conclusions arrived at in the study imply that policymakers should not make any short-term regulatory changes in the market to overcome the impact of COVID-19. Due to Covid there have been travel restrictions which have in turn impacted the oil prices. This depression can be revived and made better by the OPEC deals. The hit to oil prices has impacted the stock returns for the BRIC markets by increasing the economic policy uncertainty. Therefore, a proper economic strategy to curb additional uncertainty is required by the BRIC markets. It is advised that the investors adopt a stock-specific approach while trading. International portfolio diversification strategies may be devised in tune with the results, and investors looking for investment in these emerging market economies are likely to gain from this study.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

ORCID iD

Kunjana Malik

References

Aharon

D. Y.

, & Qadan

(2017, October). How much happiness can we find in the fear index? In Proceedings of International Academic Conferences (No. 5908141). International Institute of Social and Economic Sciences.

Ajmi

A. N.

, Hammoudeh

, Nguyen

D. K.

, & Sarafrazi

(2014). How strong are the causal relationships between Islamic stock markets and conventional financial systems? Evidence from linear and nonlinear tests. Journal of International Financial Markets, Institutions and Money , 28, 213–227.

Akdağ

(2019). Effect of VIX fear index on financial indicators: Turkey case. Hitit University Journal of Social Sciences Institute , 12(1), 235–256.

Albulescu

(2020). Coronavirus and oil price crash. https://dx.doi.org/10.2139/ssrn.3553452

Ali

, Alam

, & Rizvi

S. A. R.

(2020). Coronavirus (COVID-19)–An epidemic or pandemic for financial markets. Journal of Behavioral and Experimental Finance , 27. https://doi.org/10.1016/j.jbef.2020.100341.

Altig

, Baker

S. R.

, Barrero

J. M.

, Bloom

, Bunn

, Chen

, Davis

S. J.

, Leather

, Meyer

B. H.

, Mihaylov

, Mizen

, Parker

N. B.

, Renault

, Smietanka

, & Thwaites

(2020). Economic uncertainty before and during the COVID-19 Pandemic (Working Paper No. w27418). National Bureau of Economic Research.

Apergis

, & Apergis

(2020). Can the COVID-19 pandemic and oil prices drive the US partisan conflict index? Energy RESEARCH LETTERS , 1(1), 1–4.

Ashraf

B. N.

(2020). Stock markets’ reaction to COVID-19: Cases or fatalities? Research in International Business and Finance , 54. https://doi.org/10.1016/j.ribaf.2020.101249.

Atil

, Lahiani

, & Nguyen

D. K.

(2014). Asymmetric and nonlinear pass-through of crude oil prices to gasoline and natural gas prices. Energy Policy , 65, 567–573.

10.

Baig

, Butt

H. A.

, Haroon

, & Rizvi

S. A. R.

(2020). Deaths, panic, lockdowns and US equity markets: The case of COVID-19 pandemic. Finance Research Letters , 38. https://doi.org/10.1016/j.frl.2020.101701

11.

Bakanova

(2010). The information content of implied volatility in the crude oil market . University Of Lugano and Swiss Finance Institute.

12.

Baker

S. R.

, Bloom

, Davis

S. J.

, Kost

, Sammon

, & Viratyosin

(2020a). The unprecedented stock market reaction to COVID-19. The Review of Asset Pricing Studies , 10(4), 742–758.

13.

Baker

S. R.

, Bloom

, Davis

S. J.

, & Terry

S. J.

(2020b). Covid-induced economic uncertainty (Working Paper No. w26983). National Bureau of Economic Research.

14.

Barro

R. J.

, Ursúa

J. F.

, & Weng

(2020). The coronavirus and the great influenza pandemic: Lessons from the ‘Spanish flu’ for the coronavirus’s potential effects on mortality and economic activity (Working Paper No. w26866). National Bureau of Economic Research.

15.

Barrot

J. N.

, Grassi

, & Sauvagnat

(2020). Estimating the costs and benefits of mandated business closures in a pandemic (CEPR Discussion Papers No. 14757). Centre for Economic Policy Research.

16.

Bash

, & Alsaifi

(2019). Fear from uncertainty: An event study of Khashoggi and stock market returns. Journal of Behavioral and Experimental Finance , 23, 54–58.

17.

Basher

S. A.

, & Sadorsky

(2016). Hedging emerging market stock prices with oil, gold, VIX, and bonds: A comparison between DCC, ADCC and GO-GARCH. Energy Economics , 54, 235–247.

18.

Bhar

, & Malliaris

A. G.

(2011). Oil prices and the impact of the financial crisis of 2007–2009. Energy Economics , 33(6), 1049–1054.

19.

Cainelli

P. V.

, Pinto

A. C. F.

, & Klötzle

M. C.

(2020). Study on the relationship between the IVol-BR and the future returns of the Brazilian stock market. Revista Contabilidade & Finanças , 32(6).

20.

Cavallo

, Galiani

, Noy

, & Pantano

(2013). Catastrophic natural disasters and economic growth. Review of Economics and Statistics , 95(5), 1549–1561.

21.

Chakrabarti

, & Kumar

K. K.

(2020). High-frequency return-implied volatility relationship: Empirical evidence from Nifty and India VIX. The Journal of Developing Areas , 54(3).

22.

Chen

, & Lv

(2015). The extreme-value dependence between the crude oil price and Chinese stock markets. International Review of Economics & Finance , 39, 121–132.

23.

Chesney

, Reshetar

, & Karaman

(2011). The impact of terrorism on financial markets: An empirical study. Journal of Banking & Finance , 35(2), 253–267.

24.

Cheuathonghua

, Padungsaksawasdi

, Boonchoo

, & Tongurai

(2019). Extreme spillovers of VIX fear index to international equity markets. Financial Markets and Portfolio Management , 33(1), 1–38.

25.

Cinar

, & Uzmay

(2017). Does fear (VIX index) incite volatility in food prices? International Journal of Food and Agricultural Economics (IJFAEC) , 5(2), 69–78.

26.

Ciner

(2001). On the long run relationship between gold and silver prices A note. Global Finance Journal , 12(2), 299–303.

27.

Corbet

, Larkin

, & Lucey

(2020). The contagion effects of the Covid-19 pandemic: Evidence from gold and cryptocurrencies. Finance Research Letters , 35(1). http://doi.org/10.1016/j.frl.2020.101554.

28.

Ding

, Liu

, Zhang

, & Long

(2017). The contagion effect of international crude oil price fluctuations on Chinese stock market investor sentiment. Applied energy , 187, 27–36.

29.

Dutta

(2018). Oil and energy sector stock markets: An analysis of implied volatility indexes. Journal of Multinational Financial Management , 44, 61–68.

30.

Engle

R. F.

, & Granger

C. W.

(1987). Co-integration and error correction: Representation, estimation, and testing. Econometrica: Journal of the Econometric Society , 251–276.

31.

Gil-Alana

L. A.

, & Monge

(2020). Crude oil prices and COVID-19: Persistence of the shock. Energy RESEARCH LETTERS , 1(1). https://doi.org/10.46557/001c.13200

32.

Gjerde

Ø.

, & Saettem

(1999). Causal relations among stock returns and macroeconomic variables in a small, open economy. Journal of International Financial Markets, Institutions and Money , 9(1), 61–74.

33.

Goodell

J. W.

(2020). COVID-19 and finance: Agendas for future research. Finance Research Letters , 35. https://doi.org/10.1016/j.frl.2020.101512.

34.

Gormsen

N. J.

, & Koijen

R. S.

(2020). Coronavirus: Impact on stock prices and growth expectations (BFI Working Paper No, 2020–22). University of Chicago, Becker Friedman Institute for Economics.

35.

Granger

C. W.

(1981). Some properties of time series data and their use in econometric model specification. Journal of Econometrics , 16(1), 121–130.

36.

Hale

, Petherick

, Phillips

, & Webster

(2020). Variation in government responses to COVID-19 (Blavatnik School of Government Working Paper No. 31). Blavatnik School of Government, University of Oxford.

37.

Harris

R. I.

(1992). Testing for unit roots using the augmented Dickey-Fuller test: Some issues relating to the size, power and the lag structure of the test. Economics letters , 38(4), 381–386.

38.

Huang

, An

, Gao

, & Huang

(2015). Identifying the multiscale impacts of crude oil price shocks on the stock market in China at the sector level. Physica A: Statistical Mechanics and its Applications , 434, 13–24.

39.

Hurvich

C. M.

, & Tsai

C. L.

(1993). A corrected Akaike information criterion for vector autoregressive model selection. Journal of Time Series Analysis , 14(3), 271–279.

40.

İskenderoglu

Ö.

, & Akdag

(2020). Comparison of the effect of VIX fear index on stock exchange indices of developed and developing countries: The G20 case. South East European Journal of Economics and Business , 15(1), 105–121.

41.

Jain

, & Biswal

P. C.

(2016). Dynamic linkages among oil price, gold price, exchange rate, and stock market in India. Resources Policy , 49, 179–185.

42.

Johansen

, & Juselius

(1990). Maximum likelihood estimation and inference on cointegration with applications to the demand for money. Oxford Bulletin of Economics and Statistics , 52(2), 169–210.

43.

Kapetanios

, Pesaran

M. H.

, & Yamagata

(2011). Panels with non-stationary multifactor error structures. Journal of Econometrics , 160(2), 326–348.

44.

Lahmiri

, & Bekiros

(2020). Renyi entropy and mutual information measurement of market expectations and investor fear during the COVID-19 pandemic. Chaos, Solitons & Fractals , 139. https://doi.org/10.1016/j.chaos.2020.110084

45.

Leigh

D. A.

, Wong

J. K.

, Dehez

, & Zerbetto

(2003). Unidirectional rotation in a mechanically interlocked molecular rotor. Nature , 424(6945), 174–179.

46.

, & Wei

(2018). The dependence and risk spillover between crude oil market and China stock market: New evidence from a variational mode decomposition-based copula method. Energy Economics , 74, 565–581.

47.

Lin

J. B.

, Liang

C. C.

, & Tsai

(2019). Nonlinear relationships between oil prices and implied volatilities: Providing more valuable information. Sustainability , 11(14), 3906.

48.

Liu

, Sun

, & Zhang

(2020). Is the Chinese economy well positioned to fight the COVID-19 pandemic? The financial cycle perspective. Emerging Markets Finance and Trade , 56(10), 2259–2276.

49.

Liu

(2019). Impacts of lagged returns on the risk-return relationship of Chinese aggregate stock market: Evidence from different data frequencies. Research in International Business and Finance , 48, 243–257.

50.

Mehta

, & David

(2019). A study on cross correlation among India VIX, oil price and gold price: Evidence from India. Asian Journal of Management , 10(3), 161–166.

51.

Mensi

, Hkiri

, Al-Yahyaee

K. H.

, & Kang

S. H.

(2018). Analyzing time–frequency co-movements across gold and oil prices with BRICS stock markets: A VaR based on wavelet approach. International Review of Economics & Finance , 54, 74–102.

52.

Mzoughi

, Urom

, Uddin

G. S.

, & Guesmi

(2020). The effects of COVID-19 pandemic on oil prices, CO2 emissions and the stock market: Evidence from a VAR model. https://doi.org/10.3389/fpubh.2021.620875

53.

Nadal

, Szklo

, & Lucena

(2017). Time-varying impacts of demand and supply oil shocks on correlations between crude oil prices and stock markets indices. Research in International Business and Finance , 42, 1011–1020.

54.

Narayan

P. K.

(2005). The saving and investment nexus for China: evidence from cointegration tests. Applied Economics , 37(17), 1979–1990.

55.

Narayan

P. K.

, & Smyth

(2005). The residential demand for electricity in Australia: an application of the bounds testing approach to cointegration. Energy Policy , 33(4), 467–474.

56.

Nava

A. R.

, Venegas-Martínez

, Coronado

, & Rojas

(2018). Oil prices and stock market returns: A comparison among Brazil, Chile, and Mexico. In

Coronado

, Rojas

, & Venegas-Martínez

(Eds.), Recent topics in time series and finance: Theory and applications in emerging markets . Universidad de Guadalajara.

57.

Onali

(2020). Covid-19 and stock market volatility. https://dx.doi.org/10.2139/ssrn.3571453.

58.

Papadamou

, Fassas

, Kenourgios

, & Dimitriou

(2020). Direct and indirect effects of COVID-19 pandemic on implied stock market volatility: Evidence from panel data analysis (MPRA Paper No. 100020). University Library of Munich.

59.

Pesaran

M. H.

, & Shin

(1996). Cointegration and speed of convergence to equilibrium. Journal of econometrics , 71(1–2), 117–143.

60.

Pesaran

M. H.

, Shin

, & Smith

R. P.

(1999). Pooled mean group estimation of dynamic heterogeneous panels. Journal of the American Statistical Association , 94(446), 621–634.

61.

Pesaran

M. H.

, Shin

, & Smith

R. J.

(2001). Bounds testing approaches to the analysis of level relationships. Journal of applied Econometrics , 16(3), 289–326.

62.

Qiang

, Lin

, Zhao

, Liu

, & Wang

(2019). The impact of international crude oil price fluctuation on the exchange rate of petroleum-importing countries: A summary of recent studies. Natural Hazards , 95(1), 227–239.

63.

Ramelli

, & Wagner

A. F.

(2020). Feverish stock price reactions to Covid-19. The Review of Corporate Finance Studies , 9(3), 622–655

64.

Ratti

R. A.

, & Vespignani

J. L.

(2013). Why are crude oil prices high when global activity is weak? Economics Letters , 121(1), 133–136.

65.

Ross

A. G.

, Crowe

S. M.

, & Tyndall

M. W.

(2015). Planning for the next global pandemic. International Journal of Infectious Diseases , 38(C), 89–94.

66.

Salisu

A. A.

, & Akanni

L. O.

(2020). Constructing a global fear index for the COVID-19 pandemic. Emerging Markets Finance and Trade , 56(10), 2310–2331.

67.

Sari

, Soytas

, & Hacihasanoglu

(2011). Do global risk perceptions influence world oil prices? Energy Economics , 33(3), 515–524.

68.

Sari

S. S.

, & Kartal

(2020). The Relationship of COVID-19 Pandemic with Gold Prices, Oil Prices and VIX Index. Erzincan University Journal of Social Sciences Institute , 13(1), 93–109.

69.

Sarwar

(2012). Is VIX an investor fear gauge in BRIC equity markets? Journal of Multinational Financial Management , 22(3), 55–65.

70.

Sarwar

(2014). US stock market uncertainty and cross-market European stock returns. Journal of Multinational Financial Management , 28, 1–14.

71.

Sarwar

, & Khan

(2017). The effect of US stock market uncertainty on emerging market returns. Emerging Markets Finance and Trade , 53(8), 1796–1811.

72.

Sarwar

, Waheed

, Sarwar

, & Khan

(2020). COVID-19 challenges to Pakistan: Is GIS analysis useful to draw solutions? Science of the Total Environment , 730, 139089.

73.

Schell

, Wang

, & Huynh

T. L. D.

(2020). This time is indeed different: A study on global market reactions to public health crisis. Journal of Behavioral and Experimental Finance , 27(C). http://doi.org/10.1016/j.jbef.2020.100349.

74.

Shevelev

A. A.

(2017). Bayesian approach to evaluate the impact of external shocks on Russian macroeconomics indicators. World of economics and management/Vestnik NSU. Series: Social and Economics Sciences , 17(1), 26–40.

75.

Singhal

, & Ghosh

(2016). Returns and volatility linkages between international crude oil price, metal and other stock indices in India: Evidence from VAR-DCC-GARCH models. Resources Policy , 50, 276–288.

76.

Smith

R. D.

(2006). Responding to global infectious disease outbreaks: Lessons from SARS on the role of risk perception, communication and management. Social Science & Medicine , 63(12), 3113–3123.

77.

Sohrabi

, Alsafi

, O’Neill

, Khan

, Kerwan

, Al-Jabir

, Iosifidisa

, & Agha

(2020). World Health Organization declares global emergency: A review of the 2019 novel coronavirus (COVID-19). International Journal of Surgery , 76, 71–76.

78.

Sun

, Ding

, Fang

, Zhang

, & Li

(2019). How do fossil energy prices affect the stock prices of new energy companies? Evidence from Divisia energy price index in China’s market. Energy , 169, 637–645.

79.

Teixeira

, Fernandes

A. S. A.

, Zuccolotto

, & do Nascimento

A. B. F.

(2020). The Brazilian government’s inaction policy in the face of the COVID-19 pandemic. GIGAPP Estudios Working Papers , 7(182–189), 533–554.

80.

Whaley

R. E.

(2000). The investor fear gauge. The Journal of Portfolio Management , 26(3), 12–17.

81.

Woodley

, Jones

S. T.

, & Reburn

J. P.

(2019). The ‘fear index’ and the effectiveness of the Piotroski model. Quarterly Journal of Finance & Accounting , 57.

82.

Yang

, Peng

, Wang

, Guan

, Jiang

, Xu

, Suri

, & Chang

(2020). The deadly coronaviruses: The 2003 SARS pandemic and the 2020 novel coronavirus epidemic in China. Journal of Autoimmunity , 109(6). http://doi.org/10.1016/j.jaut.2020.102434.

83.

Yousaf

, & Hassan

(2019). Linkages between crude oil and emerging Asian stock markets: New evidence from the Chinese stock market crash. Finance Research Letters , 31.

84.

Zaremba

, Szyszka

, Long

, & Zawadka

(2020). Business sentiment and the cross-section of global equity returns. Pacific-Basin Finance Journal , 61. https://doi.org/10.1016/j.pacfin.2020.101329.

85.

Živkov

, Njegić

, & Momčilović

(2018). Bidirectional spillover effect between Russian stock index and the selected commodities. Zbornik Radova Ekonomskog Fakulteta u Rijeci: Časopis za Ekonomsku Teoriju i Praksu , 36(1), 29–53.