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 $({\alpha }_{xy}(0)=-1.14\%)$. 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.

本文通过非线性互相关和相似性的角度揭示了加密货币因 COVID-19 冲击而遭受的变化。考虑到交易量和市值，我们收集了七种最大的加密货币的每日价格和交易量数据。对于这两个属性（价格和交易量），我们计算它们的波动性并计算多重分形去趋势互相关 (MF-DCCA)，以估计描述基础过程多重分形程度的复杂性参数。我们检测（在 COVID-19 之前和期间）这些波动性时间序列对的标准多重分形行为以及整体持续的长期相关性。然而，价格波动时间序列对的多重分形比成交量波动时间序列对表现出更持久的行为。从财务角度来看，它揭示了价格的波动性时间序列对的特点是非线性互相关性的增加，不包括比特币与狗狗币对$（{\alpha }_{Xy}（0）=-1。14\%）$。同时，所有考虑成交量属性的波动时间序列对都以非线性互相关性的下降为标志。K 均值技术表明，这些价格属性的波动性时间序列能够抵御 COVID-19 的冲击。而对于这些交易量属性的波动时间序列，我们发现 COVID-19 冲击推动了加密货币群体的变化。