Long-range correlations and asymmetry in the Bitcoin market

Alvarez-Ramı́rez, José; Rodríguez, Eduardo; Ibarra-Valdez, Carlos

doi:10.1016/j.physa.2017.11.025

Cited by 161 publications

(85 citation statements)

References 14 publications

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“…Bouri, Azzi, and Dyhrberg (2017) report evidence of a negative relation between the U.S. implied volatility index and Bitcoin volatility. Alvarez-Ramirez, Rodriguez, and Ibarra-Valdez (2017) show that the Bitcoin market exhibits periods of efficiency alternating with periods where the price dynamics are driven by antipersistence. Phillip, Chan, and Peiris (2017) show that Bitcoin exhibits many diverse stylized facts such as heteroscedasticity and long memory.…”

Section: Literature Reviewmentioning

confidence: 98%

“…Bariviera (2017) uses the Hurst exponent and finds that Bitcoin price volatility exhibits a persistent behaviour and clustering. Alvarez-Ramirez, Rodriguez, and Ibarra-Valdez (2017) show that the Bitcoin market exhibits periods of efficiency alternating with periods where the price dynamics are driven by antipersistence. Lahmiri and Bekiros (2018) find that Bitcoin prices and returns display long-range correlations and multifractality.…”

Section: Literature Reviewmentioning

confidence: 98%

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Modelling long memory volatility in the Bitcoin market: Evidence of persistence and structural breaks

et al. 2018

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Motivated by the emergence of Bitcoin as a speculative financial investment, the purpose of this paper is to examine the persistence in the level and volatility of Bitcoin price, accounting for the impact of structural breaks. Using parametric and semiparametric techniques, we find strong evidence in favour of a permanency of the shocks and lack of mean reversion in the level series. We also reveal evidence of structural changes in the dynamics of Bitcoin. After accounting for the structural breaks in the level series, evidence of mean reversion is uncovered in some cases. Further analyses show evidence of a long memory in the two measures of volatility (absolute and the squared returns), whereas some cases of short memory are revealed in the squared returns series in particular. Practical implications are discussed on the inefficiency in the Bitcoin market and its importance for Bitcoin users and investors.

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Section: Literature Reviewmentioning

confidence: 98%

Section: Literature Reviewmentioning

confidence: 98%

Modelling long memory volatility in the Bitcoin market: Evidence of persistence and structural breaks

et al. 2018

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“…Jiang et al (2018) and Al-Yahyaee et al (2018) also report inefficiency caused by persistence in the time series for returns. Alvarez-Ramirez et al (2018) reports that the Bitcoin market from 2013 to 2017 is not uniformly efficient since anti-persistence of price returns appears cyclically. Based on the efficiency index, Kristoufek (2018) finds strong evidence that the Bitcoin markets remained mostly inefficient between 2010 and 2017.…”

Section: Introductionmentioning

confidence: 99%

Market Efficiency, Liquidity, and Multifractality of Bitcoin: A Dynamic Study

Takaishi

Adachi

2019

Asia-Pac Financ Markets

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This letter investigates the dynamic relationship between market efficiency, liquidity, and multifractality of Bitcoin. We find that before 2013 liquidity is low and the Hurst exponent is less than 0.5, indicating that the Bitcoin time series is anti-persistent. After 2013, as liquidity increased, the Hurst exponent rose to approximately 0.5, improving market efficiency. For several periods, however, the Hurst exponent was found to be significantly less than 0.5, making the time series anti-persistent during those periods. We also investigate the multifractal degree of the Bitcoin time series using the generalized Hurst exponent and find that the multifractal degree is related to market efficiency in a non-linear manner.

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“…Many attempts were made so far to study the market efficiency of various cryptocurrency markets, but the vast majority of the known work has been exclusively directed towards the Bitcoin market. For example, Bariviera (2017) studied the long-range memory of the Bitcoin market by analysing the Hurst exponent via the R/S and detrended-fluctuation-analysis (DFA) methods, and confirmed that daily volatility exhibits long-range memory; Alvarez-Ramirez et al (2018) implemented the DFA method to estimate the long-range dependence of Bitcoin and found that the Bitcoin market exhibited periods of efficiency, alternating in different periods; Tiwari et al (2018) reported that the Bitcoin market is informationally efficient, by using a battery of robust long-range dependence estimators; Khuntia and Pattanayak (2018) examined the efficiency of the Bitcoin market by using the Dominguez-Lobato consistent test and generalized spectral test, and concluded that dynamic efficiency in the Bitcoin market actually follows the proposition of adaptive market hypothesis (AMH); Jiang et al (2018) employed the generalised Hurst exponent to investigate long-term memory in the Bitcoin market, and results suggested that the Bitcoin market was inefficient over the whole sample period; Zhang et al (2018a) illustrated that the nine most popular cryptocurrency markets were inefficient by employing a battery of efficiency tests, and the MF-DFA and MF-DCCA approaches; Zhang et al (2018b) analysed the stylised facts of cryptocurrencies in terms of long-range dependence by using the Hurst exponent with both the R/S and DFA methods for high-frequency-return data of the four most popular cryptocurrencies, while features of dependence between the different cryptocurrencies were also provided; Chu et al (2019) analysed the efficiency of the high-frequency markets of the two largest cryptocurrencies, Bitcoin and Ethereum, versus the euro and US dollar, by investigating the existence of the AMH.…”

Section: Introductionmentioning

confidence: 94%

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

Zhang

Chan

Chu

et al. 2020

JRFM

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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.

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Long-range correlations and asymmetry in the Bitcoin market

Cited by 161 publications

References 14 publications

Modelling long memory volatility in the Bitcoin market: Evidence of persistence and structural breaks

Modelling long memory volatility in the Bitcoin market: Evidence of persistence and structural breaks

Market Efficiency, Liquidity, and Multifractality of Bitcoin: A Dynamic Study

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

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