Do bitcoins follow a random walk model?

On the (in)efficiency of cryptocurrencies: have they taken daily or weekly random walks?

“…The results obtained from both ADF and PP unit root tests reject the random walk null hypothesis at critical levels of at least 5 percent for all cryptocurrencies regardless of whether the tests are performed with a drift only or inclusive of a trend, or whether the tests are performed on daily or weekly series. Note that this evidence against weak form market efficiency in the cryptomarkets has been previously established in the works of Latif et al (2017) and Aggarwal (2019) who use similar ADF and PP unit root testing procedures albeit on weekly series. Moreover, these results are also consistent with findings obtained from the cross-sectional dependent, panel unit root tests employed by Hu et al (2019).…”

“…Our analytical approach is centred on informational efficiency, particularly, the weak form of the efficient markets theory in finance which is grounded in the random walk hypothesis of Nobel laureates Paul Samuelson and Eugene Fama. Our study contributes to the existing literature, Kristoufek (2015a,b), Dyhberg (2016), Urquhart (2016), Bariviera et al (2017), Kurihara and Fukushima (2017), Nadarajah and Chu (2017), Chu et al (2017), Latif et al (2017), Katsiampa (2017), Peng et al (2018), Ardia (2018), Tiwari et al (2018Tiwari et al ( , 2019, , , Mensi (2018), Caporale and Zekokh (2019), Aggarwal (2019), Hu et al (2019), Jana et al, 2019, Bundi andWildi (2019), in three ways. Firstly, unlike a majority if previous studies which tend to focus on singular cryptocurrencies such as Bitcoin (see Bouri et al (2019); Bouoiyour and Selmi (2016); Bariviera et al (2017); Nadarajah and Chu (2017); Troster (2018); Tiwari et al (2018); ; Alvarez-Ramirez et al (2018); Aggarwal (2019)), our study examines market efficiency in 5 cryptocurrency markets (i.e. Bitcoin (BTC), Ethereum (ETH), Litecoin, Bitcoin Cash (BTCC) and Ripple (RIP)).…”

“…The FFF-based unit root tests are considered superior testing procedures since the econometrician does not need to know a prior the form of nonlinearity underlying the time series or the number of structural breaks which exist in the data. Thirdly, instead of focusing on one frequency of time series, for instance on daily series as is the case in an overwhelming majority of existing empirical works (see Bouoiyour and Selmi (2016), Nadarajah and Chu (2017), Latif et al (2017), , Hu et al (2019) and Aggarwal (2019)), our study employs both weekly and daily series to increase the robustness of our empirical analysis. As earlier argued by Martikainen et al (1994) and Huang and Hoje (1995), if the underlying statistical properties for alternative return intervals for asset prices significantly differs from each other, then the efficiency of stock markets is contingent on the frequency (of) trading strategies employed by market participants.…”

Bitcoin fluctuations and the frequency of price overreactions

“…Similar conclusions are reached by Bouri et al (2016) and Catania and Grassi (2017). Aggarwal (2019) examines Bitcoin returns and finds strong evidence of market inefficiency (see also Urquhart 2016). Calendar anomalies in the cryptocurrency market are analysed by Kurihara and Fukushima (2017) and Caporale and Plastun (2018c), This table presents the average daily price amplitude in different financial markets.…”

A Survey on Efficiency and Profitable Trading Opportunities in Cryptocurrency Markets

“…There is also a number of recent academic papers concerning efficiency in cryptocurrencies, such as Aggarwal (2019), Bouri et al (2019), and Zargar and Kumar (2019b). Aggarwal (2019) examines efficiency in Bitcoin markets by employing daily Bitcoin prices about the period from 19 July 2010 until 20 March 2018. In order to do so, he employs serial correlation coefficient tests, unit root tests and the ARCH test.…”