Econophysics of cryptocurrency crashes: an overview

Abstract

Cryptocurrencies refer to a type of digital asset that uses distributed ledger, or blockchain technology to enable a secure transaction. Like other financial assets, they show signs of complex systems built from a large number of nonlinearly interacting constituents, which exhibits collective behavior and, due to an exchange of energy or information with the environment, can easily modify its internal structure and patterns of activity. We review the econophysics analysis methods and models adopted in or invented for financial time series and their subtle properties, which are applicable to time series in other disciplines. Quantitative measures of complexity have been proposed, classified, and adapted to the cryptocurrency market. Their behavior in the face of critical events and known cryptocurrency market crashes has been analyzed. It has been shown that most of these measures behave characteristically in the periods preceding the critical event. Therefore, it is possible to build indicators-precursors of crisis phenomena in the cryptocurrency market.

Complex systems and physics education

“…Having visualized the series we study in this paper, we can already be convinced of the non-Gaussian dynamics of the presented systems. In the course of our research, we have determined that the Lévy 𝛼-stable distribution most successfully covers the key statistical characteristics of both the economic [15][16][17] and those systems presented in this paper. Figures 3a to 3c show the window dynamics of the 𝛼 index derived from the Lévy distribution that characterizes the "heaviness" of tails.…”

“…It is worth noting that there are also various algorithms for converting a time series to a graph. We would like to emphasize the visibility graph algorithms [14,17,55,64,93] (figure 5) and one based on recurrence analysis [35] (figure 6).…”

“…The tools of quantitative recurrent analysis include the recurrence rate, determined by the ratio of recurrent points to the total number of points on the recurrence matrix under study. In addition to the recurrence measure, in the course of analyzing complex systems, it would be possible to present such measures as determinism, divergence, entropy, trend, and so on [13,17,31,40,69,92,93].…”

Stability Analysis of Bitcoin using Recurrence Quantification Analysis

“…In the literature there are few studies applying RQA methodology to Bitcoin. These are Belinskiy (2018, 2019), Kucherova et al (2021) and Bielinskyi and Serdyuk (2021). However, focus of these studies is not to evaluate the dynamical stability of the Bitcoin and these studies do not evaluate the full spectrum of RQA measures but only consider a small subset of RQA measures.…”

“…Also, data utilized in these studies do not cover recent 2021 data. Focus of Belinskiy (2018, 2019) and Bielinskyi and Serdyuk (2021) is to evaluate the suitability of RQA measures as precursors of crisis and crashes in cryptocurrency market. Focus of Kucherova et al (2021) is to reveal the relationship between the time series of the price of Bitcoin and the frequency of online requests for Bitcoin.…”