Are Cryptocurrencies Real Financial Bubbles? Evidence from Quantitative Analyses
14 Pages Posted: 27 Dec 2017 Last revised: 24 Feb 2018
Date Written: February 24, 2018
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 which is mainly driven by investor sentiment, becoming a potential source of financial bubbles and instabilities.
In this work, we draw upon the close relationship between statistics, physics and mathematical finance to 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 strong bubble signals, starting in May-September 2017 and reaching a critical time in mid December 2017. Ether, instead, presents bubble signals in mid-June 2017, corresponding to the crash actually observed on 12th June, while a second weaker signal is found by JLS-GLS only around 12th January 2018, anticipating the large crash observed in the same days.
These findings are consistent with the large crash (-30%) observed in the cryptocurrency markets between 17th and 22nd December 2017. Further study on other cryptocurrencies and Initial Coin Offerings (ICOs), an innovative structure for raising funds to support new ideas and ventures, is in progress.
Keywords: Cryptocurrency, Digital Currency, Bitcoin, Ether, Ethereum, Crisis, Bubble, Crash, Time Series, Forecast, PSY, Philips-Shi-Yu, JLS, Johansen-Ledoit-Sornette, Super-Exponential, Log-Periodic Power Law, LPPL, Calibration, Genetic Algorithm, Fit
JEL Classification: C22, C32, C51, C53, C58, E41, E42, E47, E51, G1, G17
Suggested Citation: Suggested Citation