Tu Chengyi, D'Odorico Paolo, Suweis Samir
School of Ecology and Environmental Science, Yunnan University, Kunming 650091, People's Republic of China.
Department of Environmental Science, Policy, and Management, University of California, Berkeley, CA 94720, USA.
R Soc Open Sci. 2020 Mar 18;7(3):191450. doi: 10.1098/rsos.191450. eCollection 2020 Mar.
The year 2017 saw the rise and fall of the crypto-currency market, followed by high variability in the price of all crypto-currencies. In this work, we study the abrupt transition in crypto-currency residuals, which is associated with the critical transition (the phenomenon of critical slowing down) or the stochastic transition phenomena. We find that, regardless of the specific crypto-currency or rolling window size, the autocorrelation always fluctuates around a high value, while the standard deviation increases monotonically. Therefore, while the autocorrelation does not display the signals of critical slowing down, the standard deviation can be used to anticipate critical or stochastic transitions. In particular, we have detected two sudden jumps in the standard deviation, in the second quarter of 2017 and at the beginning of 2018, which could have served as the early warning signals of two major price collapses that have happened in the following periods. We finally propose a mean-field phenomenological model for the price of crypto-currency to show how the use of the standard deviation of the residuals is a better leading indicator of the collapse in price than the time-series' autocorrelation. Our findings represent a first step towards a better diagnostic of the risk of critical transition in the price and/or volume of crypto-currencies.
2017年见证了加密货币市场的兴衰，随后所有加密货币的价格出现了高度波动。在这项工作中，我们研究了加密货币残差的突然转变，它与临界转变（临界减速现象）或随机转变现象相关。我们发现，无论具体的加密货币或滚动窗口大小如何，自相关总是在一个高值附近波动，而标准差则单调增加。因此，虽然自相关没有显示出临界减速的信号，但标准差可用于预测临界或随机转变。特别是，我们在2017年第二季度和2018年初检测到标准差的两次突然跃升，这可能是随后时期发生的两次重大价格暴跌的预警信号。我们最终提出了一个加密货币价格的平均场唯象模型，以展示残差标准差的使用如何比时间序列的自相关更好地作为价格暴跌的领先指标。我们的发现代表了朝着更好地诊断加密货币价格和/或交易量临界转变风险迈出的第一步。
