On Bitcoin markets (in)efficiency and its evolution

By Ladislav Kristoufek. Physica A: Statistical Mechanics and its Applications, 2018

182 citations recorded by the source corpus.

Abstract We study efficiency of two Bitcoin markets (with respect to the US dollar and Chinese yuan) and its evolution in time. As inefficiency can manifest through various channels, we utilize the Efficiency Index of Kristoufek & Vosvrda (2013) which can cover different types of (in)efficiency measures. We find strong evidence of both Bitcoin markets remaining mostly inefficient between 2010 and 2017 with exceptions of several periods directly connected to cooling down after the bubble-like price surges.

Subfields: Economics and Econometrics, Finance, Information Systems

View Paper: https://www.semanticscholar.org/paper/b61d0259f2c02c945f2946679f0347e36285fc70

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Citations in the corpus

Efficiency of the financial markets during the COVID-19 crisis: Time-varying parameters of fractional stable dynamics (2020, methodology): “Other indicators measure a deviation from a random walk, using for example variance ratios [15] or a combination of several statistics such as fractal dimension and entropy [47, 46].”
Effects of the geopolitical risks on Bitcoin returns and volatility (2019, background): “Cheah et al. (2018); Jiang et al. (2018), and Nadarajah and Chu (2017) show that Bitcoin markets are inefficient; while Kristoufek (2018) and Tiwari et al. (2018) also provide supporting evidence on the inefficiency of Bitcoin markets with exceptions of some periods.”
Cryptocurrency market contagion: Market uncertainty, market complexity, and dynamic portfolios (2019, background): “…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 al., 2018; Kristoufek, 2018; Jiang et al., 2018).”
Exogenous drivers of Bitcoin and Cryptocurrency volatility – A mixed data sampling approach to forecasting (2019, background): “…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 research on the…”