加密货币是真正的金融泡沫吗？来自定量分析的证据
Are cryptocurrencies real financial bubbles? Evidence from quantitative analyses
AI总结：
研究比特币和以太坊是否为金融泡沫，运用结合LPPL模型与统计模型的方法，采用多种测试版本，发现特定时间段内两种加密货币有泡沫迹象，证实其投机泡沫高风险，方法具通用性可用于金融时间序列及相关策略。
AI中文摘要：
点对点交易和区块链技术的发展促使加密货币激增，投资者数量大幅增加。加密货币价格主要由投资者情绪驱动，成为金融泡沫和不稳定的潜在来源。本文应用定量模型研究比特币和以太坊这两种最著名的加密货币。泡沫检测方法结合了Log Periodic Power Law（LPPL）模型和统计模型。具体采用了三种不同版本的JLS模型和两种PSY统计测试。研究发现，在2016年12月1日至2018年1月16日期间，比特币在2017年12月中旬和2018年1月上半叶显示出泡沫阶段的典型特征，以太坊在2017年6月中旬和2018年1月12日左右也显示出泡沫迹象。本文证实了加密货币存在与投资者过度热情相关的投机泡沫高风险，该方法具有通用性，可应用于任何金融时间序列，支持投资和风险管理策略。
英文摘要：
The growth of peer-to-peer exchanges and the blockchain technology has led to a proliferation of cryptocurrencies and to a massive increase in the number of investors who actually negotiate digital money. Cryptocurrencies trade at prices mainly driven by investor sentiment, becoming a potential source of financial bubbles and instabilities. In this work, we apply quantitative models to the study of Bitcoin and Ether, two of the most famous cryptocurrencies. Our bubble detection methodology combines the Log Periodic Power Law (LPPL) model, originally created by Johansen, Ledoit and Sornette (JLS), and the statistical model developed by Phillips, Shi, and Yu (PSY). In particular, we employ three different versions of JLS model, i.e. Ordinary Least Square (OLS), Generalised Least Squares (GLS) and Maximum Likelihood Estimation (MLE), and two PSY statistical tests (BSADF and BSADF*). We find that, during the sample period 1st December 2016 - 16th January 2018, Bitcoin shows typical hallmarks of a bubble phase in mid December 2017 and in the first half of January 2018, anticipating the large crashes observed thereafter. Also the Ether price dynamics reveals bubble evidence in mid June 2017, anticipating the crash observed on 12th June, and a weaker signal around 12th January 2018, anticipating the crash observed in the same days. This paper confirms the high risk of speculative bubbles associated with cryptocurrencies, related to investor exuberance pumping market prices far away from their fundamental values, thus creating critical situations subject to possible crashes. Our methodology is general and can be applied to virtually any financial time series, and may support investing and risk management strategies.
