James Nick, Menzies Max, Chan Jennifer
School of Mathematics and Statistics, University of Sydney, NSW, Australia.
Yau Mathematical Sciences Center, Tsinghua University, Beijing, China.
Physica A. 2021 Mar 1;565:125581. doi: 10.1016/j.physa.2020.125581. Epub 2020 Nov 25.
This paper introduces new methods for analysing the extreme and erratic behaviour of time series to evaluate the impact of COVID-19 on cryptocurrency market dynamics. Across 51 cryptocurrencies, we examine extreme behaviour through a study of distribution extremities, and erratic behaviour through structural breaks. First, we analyse the structure of the market as a whole and observe a reduction in self-similarity as a result of COVID-19, particularly with respect to structural breaks in variance. Second, we compare and contrast these two behaviours, and identify individual anomalous cryptocurrencies. Tether (USDT) and TrueUSD (TUSD) are consistent outliers with respect to their returns, while Holo (HOT), NEXO (NEXO), Maker (MKR) and NEM (XEM) are frequently observed as anomalous with respect to both behaviours and time. Even among a market known as consistently volatile, this identifies individual cryptocurrencies that behave most irregularly in their extreme and erratic behaviour and shows these were more affected during the COVID-19 market crisis.
本文介绍了分析时间序列极端和不稳定行为的新方法,以评估新冠疫情对加密货币市场动态的影响。我们对51种加密货币进行研究,通过分析分布极值来考察极端行为,并通过结构突变来考察不稳定行为。首先,我们分析了整个市场的结构,发现由于新冠疫情,自相似性降低,尤其是在方差的结构突变方面。其次,我们对这两种行为进行比较和对比,识别出个别异常的加密货币。泰达币(USDT)和真美元(TUSD)在回报方面始终是异常值,而霍洛币(HOT)、奈克索斯币(NEXO)、Maker(MKR)和新经币(XEM)在行为和时间方面都经常被视为异常。即使在一个向来波动剧烈的市场中,这也识别出了在极端和不稳定行为中表现最不规则的个别加密货币,并表明这些加密货币在新冠疫情市场危机期间受到的影响更大。