英文标题：
《Real-time Prediction of Bitcoin Bubble Crashes》
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作者：
Min Shu, Wei Zhu
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最新提交年份：
2019
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英文摘要：
  In the past decade, Bitcoin as an emerging asset class has gained widespread public attention because of their extraordinary returns in phases of extreme price growth and their unpredictable massive crashes. We apply the log-periodic power law singularity (LPPLS) confidence indicator as a diagnostic tool for identifying bubbles using the daily data on Bitcoin price in the past two years. We find that the LPPLS confidence indicator based on the daily Bitcoin price data fails to provide effective warnings for detecting the bubbles when the Bitcoin price suffers from a large fluctuation in a short time, especially for positive bubbles. In order to diagnose the existence of bubbles and accurately predict the bubble crashes in the cryptocurrency market, this study proposes an adaptive multilevel time series detection methodology based on the LPPLS model and finer (than daily) timescale for the Bitcoin price data. We adopt two levels of time series, 1 hour and 30 minutes, to demonstrate the adaptive multilevel time series detection methodology. The results show that the LPPLS confidence indicator based on this new method is an outstanding instrument to effectively detect the bubbles and accurately forecast the bubble crashes, even if a bubble exists in a short time. In addition, we discover that the short-term LPPLS confidence indicator highly sensitive to the extreme fluctuations of Bitcoin price can provide some useful insights into the bubble status on a shorter time scale - on a day to week scale, and the long-term LPPLS confidence indicator has a stable performance in terms of effectively monitoring the bubble status on a longer time scale - on a week to month scale. The adaptive multilevel time series detection methodology can provide real-time detection of bubbles and advanced forecast of crashes to warn of the imminent risk.
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中文摘要：
在过去十年中，比特币作为一种新兴资产类别，因其在极端价格增长阶段的非凡回报和不可预测的大规模崩溃而受到了公众的广泛关注。我们使用对数周期幂律奇异性（LPPLS）置信度指标作为诊断工具，利用过去两年比特币价格的每日数据识别泡沫。我们发现，基于每日比特币价格数据的LPPLS信心指数在比特币价格短期内大幅波动时，尤其是正面泡沫时，无法为检测泡沫提供有效警告。为了诊断加密货币市场中泡沫的存在并准确预测泡沫崩溃，本研究提出了一种基于LPPLS模型的自适应多级时间序列检测方法，并对比特币价格数据进行了更精细（比每日更精细）的时标。我们采用1小时和30分钟两级时间序列来演示自适应多级时间序列检测方法。结果表明，基于这种新方法的LPPLS置信度指标是一种优秀的工具，可以有效地检测气泡并准确预测气泡碰撞，即使气泡在短时间内存在。此外，我们发现，对比特币价格极端波动高度敏感的短期LPPLS信心指数可以在较短的时间尺度（从日到周的尺度）上对泡沫状态提供一些有用的见解，长期LPPLS置信度指标在较长时间尺度（从一周到一个月尺度）上有效监测泡沫状态方面表现稳定。自适应多级时间序列检测方法可以实时检测泡沫和提前预测碰撞，以警告即将发生的风险。
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分类信息：

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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一级分类：Quantitative Finance        数量金融学
二级分类：Risk Management        风险管理
分类描述：Measurement and management of financial risks in trading, banking, insurance, corporate and other applications
衡量和管理贸易、银行、保险、企业和其他应用中的金融风险
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一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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