TABLE 1.
Summary of related literature
| Study | Sample | Methodology | Main results |
|---|---|---|---|
| 1. Albulescu (2020) | US stock market from March 11, 2020 to May 15, 2020. | OLS regression, stepwise procedure | The new confirmed cases are associated with higher financial volatility. |
| 2. Baek et al. (2020) | US stock market from January 2, 2020 to April 30, 2020. | Two‐regime Markov switching model | The volatility is more sensitive to the news of COVID‐19 more than the economic indicators. Moreover, the negative COVID‐19 news (number of deaths) is twice as impactful as positive COVID‐19 news (recovered cases) suggesting a negativity bias. |
| 3. Baig et al. (2021) | US stock market from January 13, 2020 to April 17, 2020 | GARCH (1,1) model | The confirmed cases and deaths during COVID‐19 are associated with a significant upturn in volatility and market illiquidity. |
| 4. Bissoondoyal‐Bheenick et al. (2020) | G20 countries stock market from January 22, 2020 to May 20, 2020 | Bivariate fractionally integrated vector autoregressive model | The connectedness between stock return and volatility has increased during the COVID‐19 pandemic. |
| 5. Chaudhary et al. (2020) | United States, China, Japan, Germany, India, United Kingdom, France, Italy, Brazil, and Canada from January 1, 2019 to June 30, 2020 | Unit root test; the ARCH effect test; and GARCH (1,1) model | The COVID‐19 period is associated with negative mean returns for all market indices and higher volatility. |
| 6. Choi (2020) | US stock market from January 2008 to May 2020. | Wavelet coherence analysis | The economic policy uncertainty during COVID‐19 affects the sector volatility more than the global financial crisis for all sectors. |
| 7. Liu et al. (2020) | Abu Dhabi, France, Germany, United States, United Kingdom, Malaysia, Indonesia, Korea, Russia, Japan, Australia, Canada, Singapore, Taiwan, Asia ex Japan, Thailand, Hong Kong, Shanghai, Shenzhen, Italy, and India from February, 21, 2019 to March, 18, 2020. | Market model; and OLS regression | The new confirmed cases during COVID‐19 have a negative effect on stock abnormal returns. Countries in Asia, compared to other countries, experience more negative abnormal returns. |
| 8. He, Sun, et al. (2020) | China stock market from June 3, 2019, to March 13, 2020. | Average adjusted return rate model; the market index adjusted return rate model; and the market model | COVID‐19 has a negative effect on some industries; mining, electricity and heating, transportation, and environment. In contrast, other industries, for example, information technology, manufacturing, education, and health‐care are more resilient to the pandemic. |
| 9. He, Liu, et al. (2020) | Republic of China, Italy, South Korea, France, Spain, Germany, Japan, and the US stock market. | Conventional t tests; and nonparametric Mann–Whitney tests. | COVID‐19 has a negative—but short term—effect on stock markets of affected countries. |
| 10. Jelilov et al. (2020) | Nigeria stock market from February 27, 2020 to April 30, 2020. | Standard GARCH and the GJR‐GARCH model | COVID‐19 is associated with higher volatility and negative market returns. |
| 11. Kotishwar (2020) | United States, Spain, France, Italy, China, and India from March 11, 2020 to April, 2020 | VECM and CAAR model | All the selected indices have positively responded more in the post period after declaring the COVID‐19 as pandemic on March 11, 2020, compared with the pre‐period. |
| 12. Lyocsa and Molnár (2020) | US stock market from November 2019 and ends in May 2020, | Nonlinear smooth transition regime switching model | Market volatility tends to motivate the returns autocorrelation of during times of great volatility. |
| 13. Just and Echaust (2020) | US stock market from June 3, 2019 to June 12, 2020. | Two‐regime Markov switching model | There is a close dependence between returns and both implied correlation and implied volatility but not with liquidity. |
| 14. Mazur et al. (2020) | US stock market for the month of March 2020. | OLS regression | There are high positive stock returns in some sectors; healthcare natural gas, software stocks, and food. However, it falls dramatically in other sectors; petroleum, real estate, entertainment, and hospitality. Furthermore, lose‐making stocks reveal a great volatility that is negatively correlated with stock returns. |
| 15. Narayan et al. (2020) | G7 countries stock market, from July 1, 2019 to April 16, 2020. | Time‐series regression model | All the government polices during COVID 19 had a positive effect on the stock markets of the G7 countries. |
| 16. Sharif et al. (2020) | US stock market from January 21, 2020 to March 30, 2020 | Continuous wavelet transform, the wavelet coherence and the wavelet‐based granger causality tests. | COVID‐19 outbreak exhibits a greater effect on the US geopolitical risk and economic uncertainty more than on the US stock market. |
| 17. Waheed et al. (2020) | Pakistani stock market from February 26, 2020 to April 17, 2020. | Auto regressive integrated moving average; and exponential smoothing (ES) approach | COVID‐19 has a positive effect on KSE‐100 index that has a positive increment in stock returns. |
| 18. Yousef (2020) | G7 stock market indices for the period 2000–2020. | GARCH; and GJR‐GARCH models | COVID‐19 is associated with higher volatility in the G7 markets. |
| 19. Zaremba et al. (2020) | Argentina, Australia, Austria, Bahrain, Belgium, Brazil, Bulgaria, Canada, Chile, China, Colombia Croatia, Cyprus, Czechia, Denmark, Egypt, and Estonia from January 1, 2020 to April 3, 2020. | Capital asset pricing model (CAPM); three‐factor model (FF); and four‐factor model (CAR). | COVID‐19 is associated with higher volatility |
| 20. Zhang et al. (2020) | United States, Italy, China Mainland, Spain, Germany, France, United Kingdom, Switzerland, Korea, South, Netherlands, Japan, and Singapore from February 7, 2020 to March 27, 2020. | Pairwise correlations | COVID‐19 is associated with higher volatility and systematic risk. |