García-Medina Andrés, C José B Hernández
Unidad Monterrey, Centro de Investigación en Matemáticas, A.C. Av. Alianza Centro 502, PIIT, Apodaca 66628, Nuevo Leon, Mexico.
Consejo Nacional de Ciencia y Tecnología, Av. Insurgentes Sur 1582, Col. Crédito Constructor, Ciudad de México 03940 , Mexico.
Entropy (Basel). 2020 Jul 11;22(7):760. doi: 10.3390/e22070760.
We investigate the effects of the recent financial turbulence of 2020 on the market of cryptocurrencies taking into account the hourly price and volume of transactions from December 2019 to April 2020. The data were subdivided into time frames and analyzed the directed network generated by the estimation of the multivariate transfer entropy. The approach followed here is based on a greedy algorithm and multiple hypothesis testing. Then, we explored the clustering coefficient and the degree distributions of nodes for each subperiod. It is found the clustering coefficient increases dramatically in March and coincides with the most severe fall of the recent worldwide stock markets crash. Further, the log-likelihood in all cases bent over a power law distribution, with a higher estimated power during the period of major financial contraction. Our results suggest the financial turbulence induce a higher flow of information on the cryptocurrency market in the sense of a higher clustering coefficient and complexity of the network. Hence, the complex properties of the multivariate transfer entropy network may provide early warning signals of increasing systematic risk in turbulence times of the cryptocurrency markets.
我们研究了2020年近期金融动荡对加密货币市场的影响，考虑了2019年12月至2020年4月的每小时交易价格和交易量。数据被细分为不同的时间框架，并分析了通过多元转移熵估计生成的有向网络。这里采用的方法基于贪婪算法和多重假设检验。然后，我们探讨了每个子时期节点的聚类系数和度分布。结果发现，聚类系数在3月份急剧增加，这与近期全球股市暴跌最严重的时期相吻合。此外，在所有情况下，对数似然都符合幂律分布，在主要金融收缩期间估计的幂更高。我们的结果表明，从更高的聚类系数和网络复杂性的意义上来说，金融动荡会导致加密货币市场上更高的信息流。因此，多元转移熵网络的复杂特性可能为加密货币市场动荡时期系统性风险增加提供早期预警信号。
