On the Market Efficiency and Liquidity of High-Frequency Cryptocurrencies in a Bull and Bear Market

Yuanyuan Zhang, Stephen Chan, Jeffrey Chu and Hana Sulieman
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Yuanyuan Zhang: School of Mathematics, University of Manchester, Manchester M13 9PL, UK
Stephen Chan: Department of Mathematics and Statistics, American University of Sharjah, Sharjah, P.O. Box 26666, UAE
Jeffrey Chu: Department of Statistics, Universidad Carlos III de Madrid, 28903 Getafe, Spain
Hana Sulieman: Department of Mathematics and Statistics, American University of Sharjah, Sharjah, P.O. Box 26666, UAE

JRFM, 2020, vol. 13, issue 1, 1-14

Abstract: The market for cryptocurrencies has experienced extremely turbulent conditions in recent times, and we can clearly identify strong bull and bear market phenomena over the past year. In this paper, we utilise algorithms for detecting turnings points to identify both bull and bear phases in high-frequency markets for the three largest cryptocurrencies of Bitcoin, Ethereum, and Litecoin. We also examine the market efficiency and liquidity of the selected cryptocurrencies during these periods using high-frequency data. Our findings show that the hourly returns of the three cryptocurrencies during a bull market indicate market efficiency when using the detrended-fluctuation-analysis (DFA) method to analyse the Hurst exponent with a rolling window. However, when conditions turn and there is a bear-market period, we see signs of a more inefficient market. Furthermore, our results indicated differences between the cryptocurrencies in terms of their liquidity during the two market states. Moving from a bull to a bear market, Ethereum and Litecoin appear to become more illiquid, as opposed to Bitcoin, which appears to become more liquid. The motivation to study the high-frequency cryptocurrency market came from the increasing availability of higher-frequency cryptocurrency-pricing data. However, it also comes from a movement towards higher-frequency trading of cryptocurrency. In addition, the efficiency of cryptocurrency markets relates not only to whether prices are predictable and arbitrage opportunities exist, but, more widely, to topics such as testing the profitability of trading strategies and determining the maturity of cryptocurrency markets.

Keywords: Bitcoin; Ethereum; market liquidity; Hurst exponent; cryptocurrency; high frequency (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (14)

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