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QUANTIFYING THE COVID-19 SHOCK IN CRYPTOCURRENCIES

LEONARDO H. S. FERNANDES

https://orcid.org/0000-0001-8739-7511

Department of Economics and Informatics, Federal Rural University of Pernambuco, Serra Talhada, PE 56909-535, Brazil

E-mail Address: leonardo.henrique@ufrpe.br

Corresponding author.

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JOSÉ W. L. SILVA

https://orcid.org/0000-0002-9127-2866

Department of Statistics and Informatics, Federal Rural University of Pernambuco, Recife, PE 52171-900 Brazil

E-mail Address: josewesley.silva@ufrpe.br

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FERNANDO H. A. ARAUJO

https://orcid.org/0000-0001-5417-0513

Federal Institute of Education Science and Technology of Paraıba, Campus Patos PB. Acesso rodovia PB 110, S/N Alto Tubiba - CEP 58700-030 PB, Patos, Brazil

E-mail Address: fernando.araujo@ifpb.edu.br

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AURELIO F. BARIVIERA

https://orcid.org/0000-0003-1014-1010

Department of Business, ECO-SOS, Universitat Rovira i Virgili, Reus, Spain

E-mail Address: aurelio.fernandez@urv.cat

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https://doi.org/10.1142/S0218348X24500191Cited by:4 (Source: Crossref)

Abstract

This paper sheds light on the changes suffered in cryptocurrencies due to the COVID-19 shock through a nonlinear cross-correlations and similarity perspective. We have collected daily price and volume data for the seven largest cryptocurrencies considering trade volume and market capitalization. For both attributes (price and volume), we calculate their volatility and compute the Multifractal Detrended Cross-Correlations (MF-DCCA) to estimate the complexity parameters that describe the degree of multifractality of the underlying process. We detect (before and during COVID-19) a standard multifractal behavior for these volatility time series pairs and an overall persistent long-term correlation. However, multifractality for price volatility time series pairs displays more persistent behavior than the volume volatility time series pairs. From a financial perspective, it reveals that the volatility time series pairs for the price are marked by an increase in the nonlinear cross-correlations excluding the pair Bitcoin versus Dogecoin $(α_{x y} (0) = - 1.14 %)$ $(α_{x y} (0) = - 1.14 %)$ . At the same time, all volatility time series pairs considering the volume attribute are marked by a decrease in the nonlinear cross-correlations. The K-means technique indicates that these volatility time series for the price attribute were resilient to the shock of COVID-19. While for these volatility time series for the volume attribute, we find that the COVID-19 shock drove changes in cryptocurrency groups.

Keywords:

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