The inefficiency of Bitcoin revisited: a dynamic approach

By Aurelio Fernández Bariviera. 2017

563 citations recorded by the source corpus.

This letter revisits the informational efficiency of the Bitcoin market. In particular we analyze the time-varying behavior of long memory of returns on Bitcoin and volatility 2011 until 2017, using the Hurst exponent. Our results are twofold. First, R/S method is prone to detect long memory, whereas DFA method can discriminate more precisely variations in informational efficiency across time. Second, daily returns exhibit persistent behavior in the first half of the period under study, whereas its behavior is more informational efficient since 2014. Finally, price volatility, measured as the logarithmic difference between intraday high and low prices exhibits long memory during all the period. This reflects a different underlying dynamic process generating the prices and volatility.

Subfields: Economics and Econometrics, Information Systems

View Paper: https://www.semanticscholar.org/paper/719b219f9b5e2de92b2014b11289fd9d388fb046

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Citations in the corpus

What causes the attention of Bitcoin (2018, unknown): “(2018) both documenting bubbles in the Bitcoin price, Urquhart (2016), Bariviera (2017) and Nadarajah and Chu (2017) all confirm the inefficiency of Bitcoin, Katsiampa (2017) showing that the best volatility model for Bitcoin is the AR-CGARCH model, Urquhart (2017) reports price clustering in Bitcoi”
Persistence in the cryptocurrency market (2017, unknown): “Halaburda and Gandal (2014) analyse its degree of competitiveness Urquhart (2016) and Bartos (2015) focus on efficiency finding evidence for and against respectively.”
Exogenous drivers of Bitcoin and Cryptocurrency volatility – A mixed data sampling approach to forecasting (2019, result): “…Baek & Elbeck (2015) find no evidence of predictability, whereas later studies report contradicting evidence (Urquhart, 2016, Nadarajah & Chu, 2017, Bariviera, 2017, Kristoufek, 2018, Sensoy, 2019), which suggest the possibility to predict Bitcoin volatility.6 Lastly, our work is related to…”
Exploring the dynamic relationships between cryptocurrencies and other financial assets (2018, background): “Additionally, tremendous growth of the cryptocurrencies is driven by anonymity of the internet (Bariviera et al. [2017]), making the prices prone to speculative bubbles (e.g.,Cheah and Fry [2015a]). The burst of the bubble may create a significant volatility shock that will spillover to other financ”
Should investors include Bitcoin in their portfolios? A portfolio theory approach (2020, background): “Dwyer (2015) shows that the average monthly volatility of Bitcoin is higher than that of gold or a set of foreign currencies while Urquhart (2016) shows that Bitcoin returns are inefficient, which is supported by Nadarajah and Chu (2017) and Bariviera (2017).”
The predictive power of public Twitter sentiment for forecasting cryptocurrency prices (2020, background): “Bariviera (2017) and Tiwari et al. (2018) nd that Bitcoin's market is e cient, but it is unlikely that these results can be applied to the entire cryptocurrency market.”
Does bitcoin co-move and share risk with Sukuk and world and regional Islamic stock markets? Evidence using a time-frequency approach (2020, background): “Thus, over time, the habitat-based view of co-movement is supported, since a set of common information exists in the returns of Islamic equities and cryptocurrencies, which are held and traded by this specific subset of investors (Barberis et al., 2005).”
Some stylized facts of the cryptocurrency market (2018, methodology): “According to Bariviera (2017), we use six points to estimate the Hurst exponent.”
An application of extreme value theory to cryptocurrencies (2018, unknown): “For this reason, a lot of academic research has been conducted on Bitcoin (see, e.g., Cheah and Fry, 2015; Dyhrberg, 2016; Urquhart, 2016, 2017; Bariviera, 2017; Katsiampa, 2017; Nadarajah and Chu, 2017).”
Cryptocurrency market contagion: Market uncertainty, market complexity, and dynamic portfolios (2019, background): “On the one side, some authors provide evidence that the Bitcoin market is rather efficient (see, Urquhart, 2016; Nadarajah and Chu, 2017; Bariviera, 2017; Tiwari et al., 2018, among many others); whereas on the other, authors suggest that the market is not quite so (see, for instance Al-Yahyaee et…”