Statistical properties and multifractality of Bitcoin
By Tetsuya Takaishi. Physica A: Statistical Mechanics and its Applications, 2017
119 citations recorded by the source corpus.
Using 1-min returns of Bitcoin prices, we investigate statistical properties and multifractality of a Bitcoin time series. We find that the 1-min return distribution is fat-tailed, and kurtosis largely deviates from the Gaussian expectation. Although for large sampling periods, kurtosis is anticipated to approach the Gaussian expectation, we find that convergence to that is very slow. Skewness is found to be negative at time scales shorter than one day and becomes consistent with zero at time scales longer than about one week. We also investigate daily volatility-asymmetry by using GARCH, GJR, and RGARCH models, and find no evidence of it. On exploring multifractality using multifractal detrended fluctuation analysis, we find that the Bitcoin time series exhibits multifractality. The sources of multifractality are investigated, confirming that both temporal correlation and the fat-tailed distribution contribute to it. The influence of "Brexit" on June 23, 2016 to GBP--USD exchange rate and Bitcoin is examined in multifractal properties. We find that, while Brexit influenced the GBP--USD exchange rate, Bitcoin was robust to Brexit.
Subfields: Economics and Econometrics, Finance
View Paper: https://www.semanticscholar.org/paper/88b94e0474e244d855b8f6be70b797a8fe90ac88
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Citations in the corpus
- An empirical investigation of volatility dynamics in the cryptocurrency market (2019, unknown): “Among other authors who have studied the price volatility of cryptocurrencies are Chu et al. (2017), Liu et al. (2017), and Takaishi (2018), all of whom also used GARCH-type models, while Phillip et al. (2018) employed the stochastic volatility model.”
- Digital currency forecasting with chaotic meta-heuristic bio-inspired signal processing techniques (2019, background): “The price predictability of digital currencies can be determined by examining their intrinsic nonlinear dynamics [6-8], including fractality [9-”
- Rough volatility of Bitcoin (2019, methodology): “The MF-DFA has become a popular method to study the multifractal properties of various time series, and studies on Bitcoin have already applied this method, for example, Takaishi (2018); El Alaoui et al. (2018).”