The inefficiency of Bitcoin

Urquhart, Andrew

doi:10.1016/j.econlet.2016.09.019

Cited by 1,090 publications

(498 citation statements)

References 22 publications

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“…In the chart of USD, the Hurst exponent of price is greater than 0.5 before 2014 and wanders around 0.5 after 2014, which means USD-denominated Bitcoin is in the process of moving efficiently. This evidence supports the conclusion of Urquhart [14] and Bariviera [15]. The price Hurst exponent of RUB is less than 0.5.…”

Section: Rolling Window Analysissupporting

confidence: 79%

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Multifractal Detrended Cross‐Correlation Analysis of the Return‐Volume Relationship of Bitcoin Market

Zhang

Wang

et al. 2018

Complexity

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We investigate the cross-correlations of return-volume relationship of the Bitcoin market. In particular, we select eight exchange rates whose trading volume accounts for more than 98% market shares to synthesize Bitcoin indexes. The empirical results based on multifractal detrended cross-correlation analysis (MF-DCCA) reveal that (1) the nonlinear dependencies and power-law cross-correlations in return-volume relationship are found; (2) all cross-correlations are multifractal, and there are antipersistent behaviors of cross-correlation for q = 2; (3) the price of small fluctuations is more persistent than that of the volume, while the volume of larger fluctuations is more antipersistent; and (4) the rolling window method shows that the cross-correlations of return-volume are antipersistent in the entire sample period.

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Section: Rolling Window Analysissupporting

confidence: 79%

“…Urquhart [14] uses several tests to test the inefficiency of Bitcoin and finds that while Bitcoin is not efficient, it is in the process of becoming effective. Bariviera [15] employs a dynamic detrended fluctuation analysis approach to support Urquhart's conclusion and finds that the volatility of Bitcoin has long-term memory.…”

Section: Introductionmentioning

confidence: 99%

Multifractal Detrended Cross‐Correlation Analysis of the Return‐Volume Relationship of Bitcoin Market

Zhang

Wang

et al. 2018

Complexity

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“…He concluded that the order of magnitude of the energy power is 100 MW. Moreover, we can cite the work by Urquhart [9], who evaluated the economic performance of the Bitcoin system inferring that bitcoin returns are insufficient to cover the energy expenditure of mining operations. Previously, Hayes [10] describe the cost of production of one bitcoin, and O'Dwyer and Malone [11] analysed the bitcoin production cost until 2014 .…”

Section: Introductionmentioning

confidence: 99%

Banking on Blockchain: Costs Savings Thanks to the Blockchain Technology

2017

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This paper looks at the challenges and opportunities of implementing blockchain technology across banking, providing food for thought about the potentialities of this disruptive technology. The blockchain technology can optimize the global financial infrastructure, achieving sustainable development, using more efficient systems than at present. In fact, many banks are currently focusing on blockchain technology to promote economic growth and accelerate the development of green technologies. In order to understand the potential of blockchain technology to support the financial system, we studied the actual performance of the Bitcoin system, also highlighting its major limitations, such as the significant energy consumption due to the high computing power required, and the high cost of hardware. We estimated the electrical power and the hash rate of the Bitcoin network, over time, and, in order to evaluate the efficiency of the Bitcoin system in its actual operation, we defined three quantities: "economic efficiency", "operational efficiency", and "efficient service". The obtained results show that by overcoming the disadvantages of the Bitcoin system, and therefore of blockchain technology, we could be able to handle financial processes in a more efficient way than under the current system.

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“…Bouri et al (2016) examine persistence in the level and volatility of Bitcoin using both parametric and semiparametric techniques; they detect long memory in both measures of volatility considered (absolute and squared returns). Catania and Grassi (2017) provide further evidence of long memory in the cryptocurrency market, whilst Urquhart (2016) using the R/S Hurst exponent obtains strong evidence of anti-persistence, which indicates non-randomness of Bitcoin returns.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Cheung et al (2015), Dwyer (2014), Bouoiyour and Selmi (2015) and Carrick (2016) show that this market is much more volatile than others. Halaburda and Gandal (2014) analyse its degree of competitiveness Urquhart (2016) and Bartos (2015) focus on efficiency finding evidence for and against respectively. Anomalies in the cryptocurrency market are examined by Kurihara and Fukushima (2017) and Caporale and Plastun (2017).…”

Section: Literature Reviewmentioning

confidence: 99%

Persistence in the Cryptocurrency Market

2017

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The inefficiency of Bitcoin

Cited by 1,090 publications

References 22 publications

Multifractal Detrended Cross‐Correlation Analysis of the Return‐Volume Relationship of Bitcoin Market

Multifractal Detrended Cross‐Correlation Analysis of the Return‐Volume Relationship of Bitcoin Market

Banking on Blockchain: Costs Savings Thanks to the Blockchain Technology

Persistence in the Cryptocurrency Market

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