In this study we investigate possible long-range trends in the cryptocurrency market. We employed the Hurst exponent in a sample covering the period from 1 January 2016 to 26 March 2021. We calculated the Hurst exponent in three non-overlapping consecutive windows and in the whole sample. Using these windows, we assessed the dynamic evolution in the structure and long-range trend behavior of the cryptocurrency market and evaluated possible changes in their behavior towards an efficient market. The innovation of this research is that we employ the Hurst exponent to identify the long-range properties, a tool that is seldomly used in analysis of this market. Furthermore, the use of both the R/S and the DFA analysis and the use of non-overlapping windows enhance our research’s novelty. Finally, we estimated the Hurst exponent for a wide sample of cryptocurrencies that covered more than 80% of the entire market for the last six years. The empirical results reveal that the returns follow a random walk making it difficult to accurately forecast them.
In this study we investigate possible long-range trends in the cryptocurrency markets. Our sample includes 37 of the most important cryptocurrencies that reflect more than 80% of the relevant market. For the analysis in the empirical part, we employed the Hurst exponent, a statistical tool used to identify long range autocorrelations and memory in time series data. Our sample covers the period from January 1, 2016 to March 26, 2021. We use three non-overlapping windows for the estimation of the Hurst exponent. With these windows, we assess the dynamic evolution in the structure of the cryptocurrencies market and evaluate the move towards an efficient market. The innovation of this research is that we employ the Hurst exponent that is seldomly used in analyzing this market. Furthermore, the use of both the R/S and DFA analysis and the use of non-overlapping windows enhance our research’s novelty. Finally, we estimate the Hurst for a wide sample of cryptocurrencies that covers more than four fifths of the entire market for the last six years.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.