The Impact of the COVID-19 Pandemic on the Cryptocurrency Market
DOI:
https://doi.org/10.47743/saeb-2022-0014Keywords:
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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Copyright (c) 2022 Nidhal Mgadmi, Azza Béjaoui, Wajdi Moussa, Tarek Sadraoui
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