James Nick, Menzies Max
School of Mathematics and Statistics, University of Melbourne, Parkville, VIC 3010 Australia.
Beijing Institute of Mathematical Sciences and Applications, Tsinghua University, Beijing, 101408 China.
Nonlinear Dyn. 2022;107(4):4001-4017. doi: 10.1007/s11071-021-07166-9. Epub 2022 Jan 3.
This paper introduces new methods to study behaviours among the 52 largest cryptocurrencies between 01-01-2019 and 30-06-2021. First, we explore evolutionary correlation behaviours and apply a recently proposed turning point algorithm to identify regimes in market correlation. Next, we inspect the relationship between collective dynamics and the cryptocurrency market size-revealing an inverse relationship between the size of the market and the strength of collective dynamics. We then explore the time-varying consistency of the relationships between cryptocurrencies' size and their returns and volatility. There, we demonstrate that there is greater consistency between size and volatility than size and returns. Finally, we study the spread of volatility behaviours across the market changing with time by examining the structure of Wasserstein distances between probability density functions of rolling volatility. We demonstrate a new phenomenon of increased uniformity in volatility during market crashes, which we term .
本文介绍了研究2019年1月1日至2021年6月30日期间52种最大加密货币行为的新方法。首先,我们探索进化相关行为,并应用最近提出的转折点算法来识别市场相关性中的状态。接下来,我们考察集体动态与加密货币市场规模之间的关系——揭示了市场规模与集体动态强度之间的反比关系。然后,我们探索加密货币规模与其回报和波动性之间关系的时变一致性。在那里,我们证明规模与波动性之间的一致性大于规模与回报之间的一致性。最后,我们通过检查滚动波动率概率密度函数之间的瓦瑟斯坦距离结构,研究波动性行为在整个市场随时间的传播。我们证明了在市场崩溃期间波动性均匀性增加的新现象,我们将其称为 。