Are the Crypto Markets Shock Resilient to COVID-19? A Comparative Investigation of Trading Prices and Volumes

Abstract

The emergence of crypto currency as an alternative source of currency has been dubbed as one of the greatest phenomenon of the 21st century. Over the years, crypto currencies have grossly interrupted the traditional financial system and they continue to act as a catalyst for its revolution. However, cryptocurrencies have not been free from misery and controversies. The aim of this study is to explore and investigate empirically the impact of Covid-19 pandemic on price and volume dynamics in crypto markets. The study makes use of two data samples, but these samples are analyzed separately and independently. The first sample consists of Top Five Cryptocurrencies in terms of market capitalization (Bitcoin, Ethereum, XRP, Binance coin and Litecoin) as at 7 November 2020. The second one is made up of the Bottom Five Cryptocurrencies among the top 40 cryptocurrencies (FTX Token, Huobi Token, Filecoin, Dash and Decreed) as at 7 November 2020 again. The data among the Top Five Cryptocurrencies ranges from 2014 to 2021 and the data among the Bottom Five Cryptocurrencies ranges from 2018 to 2021.. The empirical results reveal that all crypto-currencies are integrated at order 1 i.e. I (1). The empirical analysis confirms presence of strong evidence for intra-and-inter long run relationship between price and volume dynamics within the crypto market irrespective of whether it is pre-pandemic or pandemic period. More so, there is convincing evidence from the results that much of the variance among the prices and volumes of the top five cryptocurrencies is attributed to the Bitcoin price-volume dynamics. This implies that it is critical for crypto market traders, investors and portfolio managers, before making any investment decision must consider the dynamics of price and trading volumes of BITCOIN as they hugely impact the prices and volumes of other altcoins.

Keywords

• cryptocurrencies
• cointergration
• VECM
• Covid-19
• resillience

References

1. Andersen, T.G. (1996). Return volatility and trading volume: An information flow interpretation of stochastic volatility. Journal of Finance, 51, 169-204.
2. Andrada-Félix, J., A. Fernandez-Perez and S. Sosvilla-Rivero (2020). Distant or close cousins: Connectedness between cryptocurrencies and traditional currencies volatilities. Journal of International Financial Markets, Institutions and Money, 67, 101213.
3. Aysan, A. F., A. Ul and I. Khan (2021). Bitcoin and altcoins price dependency: Resilience and co-movement. Finance Research Letters, 33, 101269.
4. Baumöhl, E. (2019). Are cryptocurrencies connected to forex? A quantile cross-spectral approach. Finance Research Letters, 29, 363–372.
5. Bessler, D. A. (1985). A survey of models of stock price-volume relationships. Journal of Financial and Quantitative Analysis, 22(1), 109-126.
6. Böhme, R., N. Christin, B. Edelman and T. Moore (2015). Bitcoin: Economics, technology, and governance. Journal of Economic Perspectives, 29(2), 213–238.
7. Bouri, E., E. Bouri, D. Gabauer, R. Gupta and A. K. Tiwari (2020). Volatility connectedness of major cryptocurrencies: The role of investor happiness. Journal of Risk and Financial Management, 13(6), 135.
8. Chemkha, R., A. BenSaïda and A. Ghorbel (2020). Connectedness between cryptocurrencies and foreign exchange markets: Implications for risk management. Journal of Multinational Financial Management, 55, 100634.

9. Clark, P.K., 1973. A subordinated stochastic process model with finite variance for speculative prices. Econometrica 41, 135–156
10. Coerbet, A., S. Gnanasekaran and S. Kumar (2022). The interlinkages between cryptocurrency price volatility and liquidity during the pandemic period. Research in International Business and Finance, 58, 101476. Conrad, C., A. Custovic and E. Ghysels (2018). Long- and short-term cryptocurrency volatility components: A GARCH-MIDAS analysis. Journal of Risk and Financial Management, 11(2), 23.
11. Copeland, T. E. (1976). A model of asset trading under the assumption of sequential information arrival. The Journal of Finance, 31(4), 1149-1168.
12. Dickey, D. A. and W. A. Fuller (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366), 427–431.
13. Elliott, B. Y. G., T. J Rothenberg and J. H. Stock (1996). Efficient tests for an autoregressive unit root. Econometrica, 64(4), 813–836.
14. ElBahrawy, A., L. Alessandretti,A. Kandler,R. Pastor-Satorras and A. Baronchelli (2017). Evolutionary dynamics of the cryptocurrency market. Scientific Reports, 7, 15750. Emna M, A. Jarboui and K. Mouakhar (2020). How the cryptocurrency market has performed during COVID-19? A multifractal analysis. Finance Research Letters, 36, 101579.
15. Epps, T. W. and M. L. Epps (1976). The Stochastic Dependence of Security Price Changes and Transaction Volumes: Implications for the Mixture-of-Distributions Hypothesis. Econometrica, 44(2), 305–321. Epps, T. W. (1978). Security price changes and transaction volumes: Reply. American Economic Review, 68(4), 698–700.
16. Farell, R. (2015). An analysis of the cryptocurrency industry. Wharton Research Scholars Journal, 130(5), 1–23. Retrieved from
17. Gallant, A. R., P. E. Rossi and G. Tauchen (1992). Stock prices and volume. Review of Financial Studies, 5(2), 199–242.
18. Gandal, N., J. T. Hamrick, T. Moore and T. Oberman (2018). Price manipulation in the Bitcoin ecosystem. Journal of Monetary Economics, 95, 86–96.
19. Granger, C. W. J. (1980). Testing for causality: A personal viewpoint. Journal of Economic Dynamics and Control, 2(2-4), 329–352.
20. Harris, G. and D. Solis (2003). Applied time series modelling and forecasting. Springer.
21. Harris, L., and E. Gurel (1986). Price and volume effects associated with changes in the S&P 500 list: New evidence for the existence of price pressures. The Journal of Finance, 41(4), 815–829.
22. Jennings, R. H., L. Starks and J. Fellingham (1981). An equilibrium model of asset trading with sequential information arrival. Journal of Finance, 36, 143–161.
23. Johansen, S. (1995). Likelihood based inference in cointegrated vector autoregressive models. Oxford University Press.
24. Karpoff, J. (1987). The relation between price changes and trading volume: a survey. J. Financ. Quant. Anal. 22, 109–126.
25. Kurka, J. (2019). Do cryptocurrencies and traditional asset classes influence each other? Finance Research Letters, 31, 38–46.
26. Kwiatkowski, D., P. C. B. Phillips, P. Schmidt and Y. Shin (1992). Testing the null hypothesis of stationarity against the alternative of a unit root. Journal of Econometrics, 54(1–3), 159–178.
27. Lütkepohl, H., P. Saikkonen and C. Trenkler (2000). New tools for econometrics: Quantification and simulation of economic processes. Springer.
28. Magro, P. (2016). What Greece can learn from bitcoin adoption in Latin America. Journal of Payments Strategy & Systems, 10(2), 132–138.
29. Meichen Chen, Qin Cong, and Zhang, Xiaoyu (2022). “Cryptocurrency price discrepancies under uncertainty: Evidence from COVID-19 and lockdown nexus”. Journal of International Money and Finance, 124, 102633. Okorie, D. I., and B. Lin (2020). Crude oil price and cryptocurrencies: Evidence of volatility connectedness and hedging strategy. Energy Economics, 87, 104703.
30. Owusu Junior, P., A. M. Adam and G. Tweneboah (2020). Connectedness of cryptocurrencies and gold returns: Evidence from frequency-dependent quantile regressions. Cogent Economics & Finance, 8(1), 1804037.
31. Peters, G. W., E. Panayi and A. Chapelle (2015). Trends in cryptocurrencies and blockchain technologies: A monetary theory and regulation perspective. SSRN Electronic Journal. Sjø, B. (2008). Cointegration analysis: A critical review. Journal of Econometrics, 146(1), 1-18.
32. Starks, L. T. and K Saatcioglu (1995). The stock price-volume relationship in emerging stock markets: The case of Latin America. International Journal of Forecasting, 14, 215–225.
33. Wooldridge, J. M. (2009). Introductory econometrics: A modern approach (4th ed.). Cengage Learning. Yi, S., Z. Xu and G. J. Wang (2018). Volatility connectedness in the cryptocurrency market: Is Bitcoin a dominant cryptocurrency? International Review of Financial Analysis, 60, 98–114.
34. Yamak, N., R. Yamak and S. Samut (2019). Causal relationship between bitcoin price volatility and trading volume: Rolling window approach. Financial Studies, 23(3), 6-20.
35. Yousaf and Ali. (2021). Dynamic spillover between green cryptocurrencies and stocks: A portfolio implication. International Review of Economics and Finance, 96, 103661.
36. Yuki, J. M. (2018). The history of trade: From bartering to digital currencies. Journal of Economic History, 77(2), 250-270.