[PDF] Homogeneity and heterogeneity of cryptocurrencies. | Semantic Scholar
Homogeneity and heterogeneity of cryptocurrencies.
@article{Liu2019HomogeneityAH,
  title={Homogeneity and heterogeneity of cryptocurrencies.},
  author={Xiao Fan Liu and Zengxian Lin and Xiao-Pu Han},
Xiao Fan Liu, Zengxian Lin, Xiao-Pu Han
Thousands of cryptocurrencies have been issued and publicly exchanged since Bitcoin was invented in 2008. The total cryptocurrency market value exceeds 300 billion US dollars as of 2019. This paper analyzes the prices, volumes, blockchain transactions, coin difficulties and public opinion popularities of 3607 actively exchanged cryptocurrencies. We aim to reveal and explain the homogeneity, i.e., the strong correlation of market performance, and the heterogeneity, i.e., the imbalance of…
Xiao Fan Liu, Xin-Jian Jiang, Si-Hao Liu, C. Tse
This article analyzes and summarizes the existing research on knowledge discovery in the cryptocurrency transactions using data mining techniques and classifies theexisting research into three aspects, i.e., transaction tracings and blockchain address linking, the analyses of collective user behaviors, and the study of individual user behaviors.
Co-Investment Network of ERC-20 Tokens: Network Structure Versus Market Performance
Si-Hao Liu, Xiao Fan Liu
Cryptocurrencies have attracted extensive attention from individual and institutional investors in recent years. In this emerging and inefficient capital market, the roles that institutional…
Cluster analysis on the structure of the cryptocurrency market via Bitcoin–Ethereum filtering
J. Song, Woojin Chang, J. Song
The leadership of the Bitcoin and Ethereum in the market, six homogeneous clusters composed of relatively less-traded cryptocurrencies, and transformation of the market structure after the announcement of regulations from various countries are discovered.
Shuyue Yi, Zishuang Xu, Gangjin Wang
Using the spillover index approach and its variants, we examine both static and dynamic volatility connectedness among eight typical cryptocurrencies. The results reveal that their connectedness…
It is shown that not only are the search queries and the prices connected but there also exists a pronounced asymmetry between the effect of an increased interest in the currency while being above or below its trend value.
Matthew R. Wilson, Aaron Yelowitz
Google Trends data is collected to examine determinants of interest in Bitcoin, and proxies for four possible clientele are constructed: computer programming enthusiasts, speculative investors, Libertarians, and criminals.
Syed Jawad Hussain Shahzad, E. Bouri, D. Roubaud, Ladislav Kristoufek, B. Lucey
Abstract This paper addresses the timely question of whether Bitcoin exhibits a safe-haven property for stock market investments during extreme market conditions and whether such a property is…
Multiscale fluctuations and complexity synchronization of Bitcoin in China and US markets
Wen Fang, Shaolin Tian, J. Wang
Abstract Bitcoin appeared as a currency, but it is treated as a speculative product due to its violent fluctuation. The main purpose of this article is to find out the fluctuations similarities and…
What can explain the price, volatility and trading volume of Bitcoin?
H. Aalborg, P. Molnár, Jon Erik de Vries
We study which variables can explain and predict the return, volatility and trading volume of Bitcoin. The considered variables are return, volatility, trading volume, transaction volume, change in…
Dehua Shen, Andrew Urquhart, Pengfei Wang
This paper adds to the growing literature of Bitcoin by examining the link between investor attention and Bitcoin returns, trading volume and realized volatility. Unlike previous studies, we employ…
P. Ciaian, M. Rajcaniova, d'Artis Kancs
This study empirically examines interdependencies between BitCoin and altcoin markets in the short- and long-run. We apply time-series analytical mechanisms to daily data of 17 virtual currencies…
The Gini Coefficient's Application to General Complex Networks
Hai-Bo Hu, L. Wang
In scale-free networks, the Gini coefficient is proved to be equivalent to the parameters mentioned above, and moreover, a classification of infinite scale- free networks is given according to the value of the GINI coefficient.