Table 2 Brief review of earlier studies on the nexus between COVID-19 outbreak and cryptocurrency market
Author(s) | Period | Variables | Quantitative methods | Empirical outcomes |
|---|---|---|---|---|
Apergis (2021) | February 1, 2020–October 31, 2021 | Bitcoin, Dash, Ethereum, Litecoin, XRP, NEM, DigiByte, Dogecoin, global established cases, global fatality cases | TGARCH, GJR-GARCH | The pandemic has a beneficial impact on the volatility of returns |
Caferra and Vidal-Tomás (2021) | November 1, 2019–June 1, 2020 | S&P 500, Euro Stoxx 50, Bitcoin, Ethereum | Wavelet coherence approach, Markov switching autoregressive model | COVID-19 caused a temporary effect on cryptocurrency dynamics |
Du (2022) | January 11, 2019, to May 1, 20,201, | Bitcoin price, daily number of newly confirmed COVID-19 cases in China and in the United States | ARMAX, GARCH | Under the ARMAX model, there is no significant link between Bitcoin price and COVID-19, but the GARCH model exhibit a significant association |
Iqbal et al. (2021) | January 1, 2020–June 15, 2020 | Top ten cryptocurrencies ac- cording to market capitalization, regular additions in the active cases, everyday addition in number of deaths | QQR (Quantile-on-Quantile Regression) | Asymmetric effect of contagion severity on the downward and Bullish prognoses in cryptocurrencies |
Jalan et al. (2021) | March 2020–August 2021 | Digix Gold Token, Perth Mint Gold Token, Tether Gold, PAX Gold, Midas Touch Gold | Tail copula, dynamic spillovers | During the COVID-19 pandemic, the volatility of gold-backed cryptocurrencies was similar to that of Bitcoin |
Keramiyan and Gokmenoglu (2022) | September 2010–June 2020 | Bitcoin prices, Macroeconomic Uncertainty Index (MUI), Economic Uncertainty Related Queries (EURQ) | Conventional Granger causality, Granger causality test in quantiles | Bitcoin can act as a hedge against macroeconomic uncertainty during prolonged financial distress |
Mariana et al. (2021) | July 1, 2019–April 6, 2020 | Bitcoin, Ethereum, gold, S&P 500 daily returns | Dynamic conditional correlation analysis, regression analysis | Bitcoin and Ethereum exhibit large daily return volatilities throughout pandemic |
Mgadmi et al. (2022) | January 2, 2019–July 27, 2021 | Bitcoin, Ethereum, Stellar, Ripple, Cardano, cases, deaths and vaccination during the pandemic | ARMA(p,q), ARCH, GARCH, EGARCH, TGARCH, Ordinary Least squares method | Except for Cardano, the overall death toll has a negative effect on cryptocurrencies’ price The overall population with the disease and the total population who have received vaccinations have a positive impact on the cryptocurrency market |
Mnif et al. (2020) | April 29, 2013–May 19, 2020 | Bitcoin, Ethereum, Ripple, Litecoin, Binance | Multifractal analysis | COVID-19 improved the efficiency of the digital currencies |
Raza et al. (2022a) | January 19, 2020–April 26, 2021 | Ethereum, Stellar, Bitcoin, Ripple, Binance Coin, Litecoin, Cardano, Chain Link | Time-varying parameter vector autoregressions (TVP-VAR), causality-in-quantiles | The spillover connectedness across the virtual currencies is significantly impacted by COVID-19 |
Sahoo (2021) | March 10, 2020–June 30, 2020 | Bitcoin, Ethereum, Bitcoin Cash, Ripple, Litecoin, COVID-19 established and death cases | Linear and nonlinear Granger causality | Unidirectional causal relation from COVID-19 figures to cryptocurrency price returns |
Sahoo and Rath (2022) | March 15, 2020–December 15, 2021 | Bitcoin, total number of confirmed cases and total number of deaths caused by the COVID-19 | Frequency-domain granger causality | The association between the overall number of reported COVID-19 cases and Bitcoin returns was only ascertained at short and medium frequency bands |
Vidal-Tomás (2021) | August 1, 2019–August 1, 2020 | 69 cryptocurrencies | Network analysis | The virtual currency market was impacted by COVID-19 on March 12, 2020, but the market has gradually rebounded to its original conditions since April 2020 |
Wasiuzzaman and Rahman (2021) | October 2, 2019–September 28, 2020 | Gold-backed cryptocurrencies | ARMA-GARCH model | The average yields and volatility for both PAX Gold and Gold are larger during the pandemic and bear market stages, but the effect is non-significant |
Yan et al. (2022) | September 8, 2017–February 14, 2022 | Ten cryptocurrencies | Generalized auto-regressive conditional heteroscedasticity (GARCH) model, dynamic conditional correlation (DCC) | COVID-19 had a beneficial impact on crypto returns |