Multifractal cross-correlations of bitcoin and ether trading characteristics in the post-COVID-19 time
后疫情时期比特币与以太币交易特征的多重分形交叉相关AI 解读 ↓
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分类依据与纠错
AI 根据所提供的论文材料归类：摘要明确使用加密货币高频交易数据研究收益、交易笔数与成交量的时间相关。
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后疫情时期比特币与以太币交易特征的多重分形交叉相关
- 研究问题
- 研究比特币与以太币的收益、交易笔数和成交量是否具有多重分形相关，以及两种资产的同步与滞后相关如何随时间尺度变化。
- 方法与思路
- 作者使用两种加密货币的高频交易数据，开展多重分形去趋势交叉相关分析，并比较同步记录和相互错位后的时间序列。
- 材料陈述的贡献
- 摘要称各项交易特征均呈现单变量及双变量多重分形结构；时间错位部分削弱短尺度相关，而长尺度相关趋于相同。
- 阅读关注点
- 应核对样本时期、采样频率、尺度区间和去趋势设定，重点检查多重分形证据如何排除厚尾、非平稳性等替代解释。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未经核实；摘要未说明检验细节，交叉相关及其对称性不能直接解释为因果关系。
原文摘要
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.