摘要翻译：
我们提供了从2012年1月到2018年2月比特币对美国元价格动态的详细泡沫分析。介绍了一种鲁棒的自动峰值检测方法，该方法将价格时间序列分为不间断市场增长时期和不间断市场下降时期。结合用于检测新市场制度开始的拉格朗日正则化方法，我们识别出3个主要峰值和10个额外的较小峰值，它们在分析的时间段内打断了比特币价格的动态。我们通过一些定量指标和图表解释了这种长短泡沫的分类，以理解这段时间比特币上涨背后的主要社会经济驱动力。然后，利用对数周期幂律奇异性(LPPLS)模型，基于LPPLS置信度指标（定义为LPPLS模型在多个时间窗口中合格拟合的分数），详细分析了与三个长泡沫相关的不断增长的风险。此外，对于各种虚构的“现在”时间$T_2$，我们采用聚类方法对不同时间尺度上预测的LPPLS拟合的临界时间$T_C$进行分组，其中$T_C$是泡沫结束的最可能时间。每个集群都是作为后续比特币价格演变的可信场景提出的。我们提出了三个长泡沫和四个短泡沫的预测，我们的分析时间尺度能够解决这些问题。总的来说，我们的预测方案提供了有用的信息来警告即将发生的崩溃风险。
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英文标题：
《Dissection of Bitcoin's Multiscale Bubble History from January 2012 to
  February 2018》
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作者：
Jan-Christian Gerlach, Guilherme Demos and Didier Sornette
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最新提交年份：
2019
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分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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英文摘要：
  We present a detailed bubble analysis of the Bitcoin to US Dollar price dynamics from January 2012 to February 2018. We introduce a robust automatic peak detection method that classifies price time series into periods of uninterrupted market growth (drawups) and regimes of uninterrupted market decrease (drawdowns). In combination with the Lagrange Regularisation Method for detecting the beginning of a new market regime, we identify 3 major peaks and 10 additional smaller peaks, that have punctuated the dynamics of Bitcoin price during the analyzed time period. We explain this classification of long and short bubbles by a number of quantitative metrics and graphs to understand the main socio-economic drivers behind the ascent of Bitcoin over this period. Then, a detailed analysis of the growing risks associated with the three long bubbles using the Log-Periodic Power Law Singularity (LPPLS) model is based on the LPPLS Confidence Indicators, defined as the fraction of qualified fits of the LPPLS model over multiple time windows. Furthermore, for various fictitious 'present' times $t_2$ before the crashes, we employ a clustering method to group the predicted critical times $t_c$ of the LPPLS fits over different time scales, where $t_c$ is the most probable time for the ending of the bubble. Each cluster is proposed as a plausible scenario for the subsequent Bitcoin price evolution. We present these predictions for the three long bubbles and the four short bubbles that our time scale of analysis was able to resolve. Overall, our predictive scheme provides useful information to warn of an imminent crash risk.
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PDF链接：
https://arxiv.org/pdf/1804.06261