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Stylised facts for high frequency cryptocurrency data

Yuanyuan Zhang, Stephen Chan, Jeffrey Chu and Saralees Nadarajah

Physica A: Statistical Mechanics and its Applications, 2019, vol. 513, issue C, 598-612

Abstract: The term ‘stylised facts’ has been extensively researched through the analysis of many different financial datasets. More recently, cryptocurrencies have been investigated as a new type of financial asset, and provide an interesting example, with a current market value of over $500 billion. Here, we analyse the stylised facts in terms of the Hurst exponent, using both the DFA and R/S methods, of the four most popular cryptocurrencies ranked according to their market capitalisation. The analysis is conducted on high frequency returns data with varying lags. In addition to using the Hurst exponent, our analysis also considers features of dependence between the different cryptocurrencies.

Keywords: Bitcoin; Ethereum; Hurst exponent; Tail dependence (search for similar items in EconPapers)
Date: 2019
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