The Impact of the COVID-19 Pandemic on the Cryptocurrency Market

Authors

  • Nidhal Mgadmi Faculty of Economics and Management of Mahdia, University of Monastir
  • Azza Béjaoui Higher Institute of Management of Tunis, University of Tunis
  • Wajdi Moussa Higher Institute of Management of Tunis, University of Tunis
  • Tarek Sadraoui Faculty of Economics and Management of Mahdia, University of Monastir

DOI:

https://doi.org/10.47743/saeb-2022-0014

Keywords:

Covid-19 pandemic, cryptocurrency volatility, leverage effect, cryptocurrency dynamics, econometric modeling.

Abstract

The purpose of our paper is to analyze the main factors which influence fiscal balance’s evolution and thereby identify solutions for configuring a sustainable fiscal policy. We have selected as independent variables some of the main macroeconomic measures, respectively public debt, unemployment rate, economy openness degree, population, consumer goods’ price index, current account balance, direct foreign investments and economic growth rate. Our research method uses two econometric models applied on a sample of 22 countries, respectively 14 developed and 8 emergent. The first model is a multiple regression and studies the connection between the fiscal balance and selected independent variables, whereas the second one uses first order differences and introduces economic freedom as a dummy variable to catch the dynamic influences of selected measures upon fiscal result. The time interval considered was 1999-2013. The results generated using the two models revealed that public debt, current account balance and economic growth significantly influence the fiscal balance. As a consequence, the governments need to plan and implement a fiscal policy which resonates with economy priorities and the phase of the economic cycle, as well as ensure a proper management of the public debt, stimulate sustainable economic growth and employment.

References

Aalborg, H. A., Molnar, P., & de Vries, J. E. (2018). What can explain the price, volatility and trading volume of Bitcoin? Finance Research Letters, 29(June), 255-265. http://dx.doi.org/10.1016/j.frl.2018.08.010

Abakah, E. J. A., Gil-Alana, L. A., Madigu, G., & Romero-Rojo, F. (2020). Volatility persistence in cryptocurrency markets under structural breaks. International Review of Economics & Finance, 69, 680-691. http://dx.doi.org/10.1016/j.iref.2020.06.035

Akhtaruzzaman, M., Sensoy, A., & Corbet, S. (2020). The influence of Bitcoin on portfolio diversification and design. Finance Research Letters, 37, 101344. http://dx.doi.org/10.1016/j.frl.2019.101344

Al Guindy, M. (2021). Cryptocurrency price volatility and investor attention. International Review of Economics & Finance, 76, 556-570. http://dx.doi.org/10.1016/j.iref.2021.06.007

Apergis, N. (2022). COVID-19 and cryptocurrency volatility: Evidence from asymmetric modelling. Finance Research Letters, 47, 102659. http://dx.doi.org/10.1016/j.frl.2021.102659

Baek, S., Mohanty, S. K., & Glambosky, M. (2020). COVID-19 and stock market volatility: An industry level analysis. Finance Research Letters, 37, 101748. http://dx.doi.org/10.1016/j.frl.2020.101748

Baur, D. G., & Dimpfl, T. (2018). Asymmetric volatility in cryptocurrencies. Economics Letters, 173, 148-151. http://dx.doi.org/10.1016/j.econlet.2018.10.008

Béjaoui, A., Mgadmi, N., Moussa, W., & Sadraoui, T. (2021). A short-and long-term analysis of the nexus between Bitcoin, social media and Covid-19 outbreak. Heliyon, 7(7), e07539. http://dx.doi.org/10.1016/j.heliyon.2021.e07539

Ben Cheikh, N., Ben Zaied, Y., & Chevallier, J. (2020). Asymmetric volatility in cryptocurrency markets: New evidence from smooth transition GARCH models. Finance Research Letters, 35, 101293. http://dx.doi.org/10.1016/j.frl.2019.09.008

Bergsli, L. Ø., Lind, A. F., Molnár, P., & Polasik, M. (2022). Forecasting volatility of Bitcoin. Research in International Business and Finance, 59, 101540. http://dx.doi.org/10.1016/j.ribaf.2021.101540

Catania, L., & Grassi, S. (2022). Forecasting cryptocurrency volatility. International Journal of Forecasting, 38(3), 878-894. http://dx.doi.org/10.1016/j.ijforecast.2021.06.005

Chen, L., Pelger, M., & Zhu, J. (2019). Deep Learning in Asset Pricing. arXiv:1904.00745. http://dx.doi.org/10.48550/arXiv.1904.00745

Chi, Y., & Hao, W. (2021). Volatility models for cryptocurrencies and applications in the options market. Journal of International Financial Markets, Institutions and Money, 75, 101421. http://dx.doi.org/10.1016/j.intfin.2021.101421

Corbet, S., Hou, Y., Hu, Y., Lucey, B., & Oxley, L. (2021). Aye Corona! The contagion effects of being named Corona during the COVID-19 pandemic. Finance Research Letters, 38, 101591. http://dx.doi.org/10.1016/j.frl.2020.101591

Cross, J. L., Hou, C., & Trinh, K. (2021). Returns, volatility and the cryptocurrency bubble of 2017–18. Economic Modelling, 104, 105643. http://dx.doi.org/10.1016/j.econmod.2021.105643

D’Amato, V., Levantesi, S., & Piscopo, G. (2022). Deep learning in predicting cryptocurrency volatility. Physica A, 596, 127158. http://dx.doi.org/10.1016/j.physa.2022.127158

Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74, 427-431. http://dx.doi.org/10.1080/01621459.1979.10482531

Dickey, D. A., & Fuller, W. A. (1981). Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root. Econometrica, 49(4), 1057-1072. http://dx.doi.org/10.2307/1912517

Elsayed, A. H., Gozgor, G., & Lau, C. K. M. (2022). Risk transmissions between bitcoin and traditional financial assets during the COVID-19 era: The role of global uncertainties. International Review of Financial Analysis, 81, 102069. http://dx.doi.org/10.1016/j.irfa.2022.102069

Fang, L., Bouri, E., Gupta, R., & Roubaud, D. (2018). Does global economic uncertainty matter for the volatility and hedging effectiveness of Bitcoin? International Review of Financial Analysis, 61(January), 29-36. http://dx.doi.org/10.1016/j.irfa.2018.12.010

Fang, T., Su, Z., & Yin, L. (2020). Economic fundamentals or investor perceptions? The role of uncertainty in predicting long-term cryptocurrency volatility. International Review of Financial Analysis, 71, 101566. http://dx.doi.org/10.1016/j.irfa.2020.101566

Goodell, J. W., & Goutte, S. (2020). Co-movement of Covid-19 and Bitcoin: Evidence from wavelet coherence analysis. Finance Research Letters, 38, 1-6. http://dx.doi.org/10.1016/j.frl.2020.101625

Haroon, O., & Rizvi, S. A. R. (2020). COVID-19: Media coverage and financial markets behavior-A sectoral inquiry. Journal of Behavioral and Experimental Finance, 27, 100343. http://dx.doi.org/10.1016/j.jbef.2020.100343

Iqbal, N., Fareed, Z., Wan, G., & Shahzad, F. (2021). Asymmetric nexus between Covid-19 outbreak in the world and cryptocurrency market. International Review of Financial Analysis, 73, 101-613. http://dx.doi.org/10.1016/j.irfa.2020.101613

James, N., Menzies, M., & Chan, J. (2021). Changes to the extreme and erratic behaviour of cryptocurrencies during COVID-19. Physica A, 565(March), 1-19. http://dx.doi.org/10.1016/j.physa.2020.125581

Kakinaka, S., & Umeno, K. (2020). Characterizing Cryptocurrency Market with Lévy’s Stable Distributions. Journal of the Physical Society of Japan, 89(2), 024802. http://dx.doi.org/10.7566/JPSJ.89.024802

Katsiampa, P. (2018). Volatility co-movement between Bitcoin and Ether. Finance Research Letters, 30(September), 221-227. http://dx.doi.org/10.1016/j.frl.2018.10.005

Katsiampa, P., Corbet, S., & Lucey, B. (2019). Volatility spillover effects in leading cryptocurrencies: A BEKK-MGARCH analysis. Finance Research Letters, 29, 68-74. http://dx.doi.org/10.1016/j.frl.2019.03.009

Kyriazis, Ν. A., Daskalou, K., Arampatzis, M., Prassa, P., & Papaioannou, E. (2019). Estimating the volatility of cryptocurrencies during bearish markets by employing GARCH models. Heliyon, 5(8), e02239. http://dx.doi.org/10.1016/j.heliyon.2019.e02239

Lahmiri, S., & Bekiros, S. (2021). The effect of COVID-19 on long memory in returns and volatility of cryptocurrency and stock markets. Chaos, Solitons, and Fractals, 151, 111221. http://dx.doi.org/10.1016/j.chaos.2021.111221

López-Cabarcos, M. Á., Pérez-Pico, A. M., Piñeiro-Chousa, J., & Šević, A. (2021). Bitcoin volatility, stock market and investor sentiment. Are they connected? Finance Research Letters, 38, 101399. http://dx.doi.org/10.1016/j.frl.2019.101399

MacKinnon, J. G. (1992). Model Specification Tests and Artificial Regressions. Journal of Economic Literature, 30(1), 102-146.

MacKinnon, J. G. (1996). Numerical distribution functions for unit root and cointegration tests. Journal of Applied Econometrics, 11, 601-618. http://dx.doi.org/10.1002/(SICI)1099-1255(199611)11:6<601::AID-JAE417>3.0.CO;2-T

Qiu, Y., Wang, Y., & Xie, T. (2021). Forecasting Bitcoin realized volatility by measuring the spillover effect among cryptocurrencies. Economics Letters, 208, 110092. http://dx.doi.org/10.1016/j.econlet.2021.110092

Salisu, A. A., & Ogbonna, A. E. (2021). The return volatility of cryptocurrencies during the COVID-19 pandemic: Assessing the news effect. Global Finance Journal, 100641. http://dx.doi.org/10.1016/j.gfj.2021.100641

Salisu, A. A., & Vinh Vo, X. (2020). Predicting stock returns in the presence of COVID-19 pandemic: The role of health news. International Review of Financial Analysis, 71, 101546. http://dx.doi.org/10.1016/j.irfa.2020.101546

Umar, Z., & Gubareva, M. (2020). A time-frequency analysis of the impact of the Covid-19 induced panic on the volatility of currency and cryptocurrency markets. Journal of Behavioral and Experimental Finance, 28, 100404. http://dx.doi.org/10.1016/j.jbef.2020.100404

Walther, T., Klein, T., & Bouri, E. (2019). Exogenous drivers of Bitcoin and Cryptocurrency volatility – A mixed data sampling approach to forecasting. Journal of International Financial Markets, Institutions and Money, 63, 101133. http://dx.doi.org/10.1016/j.intfin.2019.101133

Yin, L., Nie, J., & Han, L. (2021). Understanding cryptocurrency volatility: The role of oil market shocks. International Review of Economics & Finance, 72, 233-253. http://dx.doi.org/10.1016/j.iref.2020.11.013

Yousaf, I., & Ali, S. (2020). The COVID-19 outbreak and high frequency information transmission between major cryptocurrencies: Evidence from the VAR-DCC-GARCH approach. Borsa Istanbul Review, 20(December), S1-S10. http://dx.doi.org/10.1016/j.bir.2020.10.003

Zaremba, A., Kizys, R., Aharon, D. Y., & Demir, E. (2020). Infected Markets: Novel Coronavirus, Government Interventions, and Stock Return Volatility around the Globe. Finance Research Letters, 35(July), 101597. http://dx.doi.org/10.1016/j.frl.2020.101597

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Published

2022-09-12

How to Cite

Mgadmi, N. ., Béjaoui, A. ., Moussa, W., & Sadraoui, T. . (2022). The Impact of the COVID-19 Pandemic on the Cryptocurrency Market. Scientific Annals of Economics and Business, 69(3), 343–359. https://doi.org/10.47743/saeb-2022-0014

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