By Marco Bianchetti (Intesa Sanpaolo – Financial and Market Risk Management; University of Bologna), Camilla Ricci (Intesa Sanpaolo-Financial and Market Risk Management) & Marco Scaringi (Intesa Sanpaolo – Financial and Market Risk Management)

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 LPPL 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 – 13th December 2017, 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 the sharp rise observed in November 2017, though, is too short for our models to detected valid bubble signals.

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.

Source: SSRN