Fractional and fractal processes applied to cryptocurrencies price series

• Published in: Journal of advanced research Vol. 32; pp. 85 - 98
• Main Authors: David, S.A., Inacio Jr, C.M.C., Nunes, R., Machado, J.A.T.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-09-2021; Elsevier
• Subjects: Bitcoin; Business and management; Chaotic dynamics; Computer modeling; Prediction; Simulation; Business and management; Chaotic dynamics; Simulation; Computer modeling; Prediction; Bitcoin
• ISSN: 2090-1232; 2090-1224
• DOI: 10.1016/j.jare.2020.12.012

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•The dynamics of the cryptocurrencies is analyzed.•Several tools for investigating the dynamic behavior of the cryptocurrencies are adopted.•Characteristics of the cryptocurrencies price time series such as persistence, randomness, predictability and chaoticity are addressed.•With exception of for the Bitcoin, the other five cryptocurrencies analyzed are mean reverting.
Cryptocurrencies have been attracting the attention from media, investors, regulators and academia during the last years. In spite of some scepticism in the financial area, cryptocurrencies are a relevant subject of academic research.
In this paper, several tools are adopted as an instrument that can help market agents and investors to more clearly assess the cryptocurrencies price dynamics and, thus, guide investment decisions more assertively while mitigating risks.
We consider three methods, namely the Auto-Regressive Integrated Moving Average (ARIMA), Auto-Regressive Fractionally Integrated Moving Average (ARFIMA) and Detrended Fluctuation Analysis, and three indices given by the Hurst and Lyapunov exponents or the Fractal Dimension. This information allows assessing the behaviour of the time series, such as their persistence, randomness, predictability and chaoticity.
The results suggest that, except for the Bitcoin, the other cryptocurrencies exhibit the characteristic of mean reverting, showing a lower predictability when compared to the Bitcoin. The results for the Bitcoin also indicate a persistent behavior that is related to the long memory effect.
The ARFIMA reveals better predictive performance than the ARIMA for all cryptocurrencies. Indeed, the obtained residual values for the ARFIMA are smaller for the auto and partial auto correlations functions, as well as for confidence intervals.