论文标题
比特币和以太交易特征的多重跨相关性在19次后时间
Multifractal cross-correlations of bitcoin and ether trading characteristics in the post-COVID-19 time
论文作者
论文摘要
与价格波动不同，加密货币交易的时间结构很少是系统研究的主题。为了填补这一差距，我们分析了价格收益的降解相关性，平均交易数量以及基于高频数据的交易量，代表了两个主要的加密货币：比特币和以太币。我们采用多重划分的脱侧交叉相关分析，这被认为是识别时间序列中非线性相关性的最可靠方法。我们发现我们的研究中考虑的所有数量都表明了来自单变量（自动相关）和双变量（互相关）的明确多重结构。我们查看了比特币 - 同时记录的信号以及时置信号中的比特币，其中一个加密货币的时间序列相对于另一个加密货币发生了变化。这样的偏移会在短时间内部分抑制互相关，但并不能完全删除它们。我们没有观察到领先资产的两种选择的结果中任何定性的不对称性。对于足够长的尺度，同时和滞后时间序列的互相关变为相同。
Unlike price fluctuations, the temporal structure of cryptocurrency trading has seldom been a subject of systematic study. In order to fill this gap, we analyse detrended correlations of the price returns, the average number of trades in time unit, and the traded volume based on high-frequency data representing two major cryptocurrencies: bitcoin and ether. We apply the multifractal detrended cross-correlation analysis, which is considered the most reliable method for identifying nonlinear correlations in time series. We find that all the quantities considered in our study show an unambiguous multifractal structure from both the univariate (auto-correlation) and bivariate (cross-correlation) perspectives. We looked at the bitcoin--ether cross-correlations in simultaneously recorded signals, as well as in time-lagged signals, in which a time series for one of the cryptocurrencies is shifted with respect to the other. Such a shift suppresses the cross-correlations partially for short time scales, but does not remove them completely. We did not observe any qualitative asymmetry in the results for the two choices of a leading asset. The cross-correlations for the simultaneous and lagged time series became the same in magnitude for the sufficiently long scales.
