Asymmetric effect of COVID-19 pandemic on E7 stock indices: Evidence from quantile-on-quantile regression approach

Hashmi, Shabir Mohsin; Chang, Bisharat Hussain; Rong, Li

doi:10.1016/j.ribaf.2021.101485

Cited by 54 publications

(41 citation statements)

References 29 publications

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“…The empirical outcomes could help investors and asset managers to adjust their trading strategies. Moreover, the government should consider economic relief packages and formulate policies to lessen severe falls in prices (Hashmi et al 2021).…”

Section: Discussionmentioning

confidence: 99%

“…Czech et al (2020) reported a negative association among the Visegrad stock market indices and the COVID-19 diffusion. For the case of emerging markets, Hashmi et al (2021) advised that the number of coronavirus cases negatively influences stock prices mainly when these financial markets are in a bearish condition. Contrariwise, O'Donnell et al (2021) found that the everyday amounts of COVID-19 cases did not explain the index price variations in China, Spain, Italy, the United Kingdom, and the United States.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Prior papers were focused on volatility examination for African equity markets (Lo et al 2021;Takyi and Bentum-Ennin 2021;Zoungrana et al 2021), the Australian stock market (Brueckner and Vespignani 2021), BRICS and G7 states (Yu et al 2021), Canada and the US (Xu 2021), the Chinese stock market (Chen et al 2021;Liu et al 2021b;Shahzad et al 2021), seven emerging countries (Hashmi et al 2021), euro area stock markets (Duttilo et al 2021), the Indian financial market (Bora and Basistha 2021), the South Korea stock market (Hoshikawa and Yoshimi 2021), Thailand (Hongsakulvasu et al 2020), the Tunisian sectorial stock market (Fakhfekh et al 2021), the US stock market (Curto and Serrasqueiro 2021;Hong et al 2021), Vietnam and Philippines (Le and Tran 2021), Visegrad Group member states (Czech et al 2020), or several international markets (Al-Najjar et al 2021;Al-Qudah and Houcine 2021;Anser et al 2021;Banerjee 2021;Chowdhury et al 2021;Contessi and Pace 2021;Engelhardt et al 2021;Höhler and Lansink 2021;Rouatbi et al 2021;Szczygielski et al 2021b;Topcu and Gulal 2020;Vera-Valdés 2021;Youssef et al 2021;Zhang et al 2020). This paper aims to examine the volatility throughout the Romanian financial market during the COVID-19 pandemic.…”

Section: Introductionmentioning

confidence: 99%

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COVID-19 Pandemic and Romanian Stock Market Volatility: A GARCH Approach

Gherghina

Armeanu

Joldeș

2021

JRFM

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This paper investigates the volatility of daily returns on the Romanian stock market between January 2020 and April 2021. Volatility is analyzed by means of the representative index for Bucharest Stock Exchange (BSE), namely, the Bucharest Exchange Trading (BET) index, along with twelve companies traded on BSE. The quantitative investigation was performed using GARCH approach. In the survey, the GARCH model (1,1) was applied to explore the volatility of the BET and BSE traded shares. Conditional volatility for the daily return series showed noticeable evidence of volatility that shifts over the explored period. In the first quarter of 2020, the Romanian equity market volatility increased to a level very close to that recorded during the global financial crisis of 2007–2009. Over the next two quarters, volatility had a downward trend. Besides, after VAR estimation, no causal connection was found among the COVID-19 variables and the BET index.

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Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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COVID-19 Pandemic and Romanian Stock Market Volatility: A GARCH Approach

Gherghina

Armeanu

Joldeș

2021

JRFM

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“…The results note that both VIX and CSFB do not explain the volatility of currencies and commodities during COVID-19. Likewise, Hashmi et al (2021) explore the asymmetric impact of the COVID-19 outbreak on stock prices of the E7 economies. The findings from quantile-on-quantile regression reveal the asymmetric impact of the COVID-19 pandemic on selected stock indices.…”

Section: Introductionmentioning

confidence: 99%

The Heterogeneous Effects of COVID-19 Outbreak on Stock Market Returns and Volatility: Evidence from Panel Quantile Regression Model

et al. 2021

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The purpose of this study is to probe the impact of the novel coronavirus (COVID-19) outbreak on stock market returns and volatility in developed markets. We employ a panel quantile regression model to capture unobserved individual heterogeneity and distributional heterogeneity. The study's findings reveal that there is a heterogeneous impact of COVID-19 on stock market returns and volatility. More specifically, there is a negative impact of COVID-19 on stock returns in the bearish stock market; however, there is an insignificant impact of COVID-19 on stock returns in the bullish stock market. Furthermore, COVID-19 has a positive impact on stock market volatility across all quantiles.JEL Classification: G24, G30, O16How to Cite:Khalid, N., Zafar, R. F., Syed, Q. R., Bhowmik, R., & Jamil, M. (2021). The Heterogeneous Effects of COVID-19 Outbreak on Stock Market Returns and Volatility: Evidence from Panel Quantile Regression Model. Etikonomi, 20(2), xx – xx. https://doi.org/10.15408/etk.v20i2.20587.

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“…Finally, we apply the quantile-on-quantile (QQ) method to explore how air quality influences stock returns across different quantiles. This method combines non-parametric estimation and traditional quantile regression, which can recognize the effects between variables in specific quantiles without considering structural break and temporal-lag effects ( 46 , 47 ). Thus, our study focuses on the asymmetric characteristic of the relationship between AQ and SR using the QQ method and obtains more comprehensive results.…”

Section: Introductionmentioning

confidence: 99%

How Air Quality Affect Health Industry Stock Returns: New Evidence From the Quantile-on-Quantile Regression

Liu

Wang

Xiao

2021

Front. Public Health

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This paper discusses the asymmetric effect of air quality (AQ) on stock returns (SR) in China's health industry through the quantile-on-quantile (QQ) regression method. Compared to prior literature, our study provides the following contributions. Government intervention, especially industrial policy, is considered a fresh and essential component of analyzing frameworks in addition to investors' physiology and psychology. Next, because of the heterogeneous responses from different industries to AQ, industrial heterogeneity is thus considered in this paper. In addition, the QQ method examines the effect of specific quantiles between variables and does not consider structural break and temporal lag effects. We obtain the following empirical results. First, the coefficients between AQ and SR in the health service and health technology industries change from positive to negative as AQ deteriorates. Second, AQ always positively influences the health business industry, but the values of the coefficients are larger in good air. In addition, different from other industries, the coefficients in the health equipment industry are negative, but the values of the coefficients change with AQ. The conclusions provide important references for investors and other market participants to avoid biased decisions due to poor AQ and pay attention to government industrial policies.

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Asymmetric effect of COVID-19 pandemic on E7 stock indices: Evidence from quantile-on-quantile regression approach

Cited by 54 publications

References 29 publications

COVID-19 Pandemic and Romanian Stock Market Volatility: A GARCH Approach

COVID-19 Pandemic and Romanian Stock Market Volatility: A GARCH Approach

The Heterogeneous Effects of COVID-19 Outbreak on Stock Market Returns and Volatility: Evidence from Panel Quantile Regression Model

How Air Quality Affect Health Industry Stock Returns: New Evidence From the Quantile-on-Quantile Regression

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