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.
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.
“…Cross-correlations analysis is a hot topic in the most distinct areas of science [22,23,24,8,25,26]. Cross-correlations analysis is a hot topic in the most distinct areas of science.…”
We have examined the nonlinear cross-correlation between the São Paulo time series of the weekly price of Bioethanol and the other 14 Brazilian capitals’ time series of the weekly price of the same biofuel using the Multifractal Detrended Cross-Correlation Analysis (MFDCCA). We provide evidence of multifractal correlation properties across these prices. We implement our method using both prices and returns and show some divergence between results. We recommend using price time series when using MF-DCCA.
“…Also, we use the multifractal risk cross-correlation (MRCC) measure Fernandes et al (2022f) to investigate the relation between the 𝑊 𝑥𝑦 (width of the spectrum) and the 𝛼 𝑥𝑦 (0) (persistence or anti-persistence). We can calculate The MRCC as:…”
We analyze multifractality for green bonds, stock sector indices, and US economic sector bonds. Green bonds and US bonds show non-linear cross-correlations. We perform Multifractal Detrended Cross-Correlations Analysis (MF-DCCA) to analyze multifractal crosscorrelations and the weak version of the Efficient Market Hypothesis (EMH). Our findings are relevant to academics, financial professionals and the general public. Although green bonds are bonds used exclusively to finance sustainable investments, they are still inefficient assets. We find that bond indices for consumer staples and equity indices for information technology and the real state sector can be used to hedge investments in green bonds.
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