This paper proposes a novel two-stage VMD-based multi-scale regression to analyze various cryptocurrency attributes that are still unclear in the existing literature. In the first stage, Variational Mode Decomposition (VMD) is used to decompose the cryptocurrency prices into low, medium and high frequency modes with different attributes. In the second stage, the VMD-based multi-scale regression is proposed for these modes with selected explanatory variables. Using the proposed framework, we focus on analyzing the multiple attributes of daily Bitcoin price data as a case study. Empirical results indicate that the low-frequency mode has specific currency or long-term investment characteristics, unlike the short/medium-term investment attributes for the medium-frequency mode, while the high-frequency mode represents some speculation. Some events merely affect a single frequency mode, but others impact all frequency modes. The results of events analysis based on VMD could enhance the identification of the multiple attributes of Bitcoin. Our findings are insightful for future regulation and management of virtual currencies.

本文提出了一种新颖的基于两阶段 VMD 的多尺度回归来分析现有文献中尚不清楚的各种加密货币属性。第一阶段采用变分模态分解（VMD），将加密货币价格分解为具有不同属性的低、中、高频模式。在第二阶段，针对这些具有选定解释变量的模式提出了基于 VMD 的多尺度回归。使用提出的框架，我们专注于分析日常的多个属性比特币价格数据作为案例研究。实证结果表明，低频模式具有特定的货币或长期投资特征，不同于中频模式的短期/中期投资属性，而高频模式代表了一些投机行为。一些事件仅影响单一频率模式，但其他事件影响所有频率模式。基于VMD的事件分析结果可以增强对比特币多重属性的识别。我们的发现对未来虚拟货币的监管和管理具有深刻的见解。