Cryptocurrency Market Consolidation in 2020–2021

Abstract

Time series of price returns for 80 of the most liquid cryptocurrencies listed on Binance are investigated for the presence of detrended cross-correlations. A spectral analysis of the detrended correlation matrix and a topological analysis of the minimal spanning trees calculated based on this matrix are applied for different positions of a moving window. The cryptocurrencies become more strongly cross-correlated among themselves than they used to be before. The average cross-correlations increase with time on a specific time scale in a way that resembles the Epps effect amplification when going from past to present. The minimal spanning trees also change their topology and, for the short time scales, they become more centralized with increasing maximum node degrees, while for the long time scales they become more distributed, but also more correlated at the same time. Apart from the inter-market dependencies, the detrended cross-correlations between the cryptocurrency market and some traditional markets, like the stock markets, commodity markets, and Forex, are also analyzed. The cryptocurrency market shows higher levels of cross-correlations with the other markets during the same turbulent periods, in which it is strongly cross-correlated itself.

The impact of COVID-19 pandemic on the dynamic correlations between gold and U.S. equities: evidence from multifractal cross-correlation analysis

“…where p t m is the price quotation time series at m = 1, … , T (Wątorek et al, 2019(Wątorek et al, , 2021Kwapień et al 2021).…”

Forecasting the Price of the Cryptocurrency Using Linear and Nonlinear Error Correction Model

“…Even though Bitcoin is the most dominant cryptocurrency, and it was found to influence other cryptocurrencies by Kwapie ń et al (2021), our goal in this research was to build a model to predict Bitcoin (BTC) log-return prices based on other cryptocurrencies' prices, because BTC has strong correlations with other major cryptocurrencies, such as Ethereum (ETH) and Binance Coin (BNB). Currently, Miller and Kim (2021) have applied several deep learning time-series models to predict BTC log-return prices, but there is no standard guideline for selecting the correct deep learning tools, which requires knowledge of the topology, training method, and other parameters.…”

Multifractal Cross-Correlations of Bitcoin and Ether Trading Characteristics in the Post-COVID-19 Time

“…It has been shown that the pandemic outburst in early 2020 has some limited effect on the multifractal properties of the price returns [7]. The most important observation is that the cryptocurrencies during the outburst lost their potential to be a safe haven, because they started to be strongly cross-correlated with the regular markets [8,9]. Strength of these correlations has been varying with time, but despite the fact that there happened periods, in which the cryptocurrency market used to evolve rather independently in the post-Covid-19 time [9][10][11][12][13], the overall picture favours strong cross-correlations, especially since Fall 2021.…”

“…The most important observation is that the cryptocurrencies during the outburst lost their potential to be a safe haven, because they started to be strongly cross-correlated with the regular markets [8,9]. Strength of these correlations has been varying with time, but despite the fact that there happened periods, in which the cryptocurrency market used to evolve rather independently in the post-Covid-19 time [9][10][11][12][13], the overall picture favours strong cross-correlations, especially since Fall 2021. By the post-Covid-19 time we mean a period after the initial pandemic-related panic had simmered down and the markets started to see the pandemic as a part of daily life, i.e., starting from approximately late Spring 2020.…”