Quantifying the COVID-19 shock in cryptocurrencies

Abstract

This paper sheds light on the changes suffered in cryptocurrencies due to the COVID-19 shock through a non-linear 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 behaviour for these volatility time series pairs and an overall persistent long-term correlation. However, multifractality for price volatility time series pairs displays more persistent behaviour 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 non-linear crosscorrelations excluding the pair Bitcoin vs Dogecoin (𝛼 𝑥𝑦 (0) = −1.14%). At the same time, all volatility time series pairs considering the volume attribute are marked by a decrease in the non-linear 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.