Stylized Facts of High-Frequency Bitcoin Time Series

This paper analyzes high-frequency intraday Bitcoin data from 2019 to 2022. The Bitcoin market index exhibits two distinct periods, characterized by abrupt volatility shifts. Bitcoin returns can be described by anomalous diffusion processes, transitioning from subdiffusion for short intervals to weak superdiffusion at longer intervals. Heavy tails are captured well by q-Gaussian distributions, and the autocorrelation of absolute returns shows power law behavior. Both periods display multifractality, with Hurst exponents shifting toward 0.5 over time, indicating increased market efficiency. The time evolution of the empirical PDF of price return allows us to connect these stylized facts to the mathematical framework of multifractals and locally fractional porous medium equations.

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Publication Details

Journal
Fractal and Fractional
Published
2025-09-29
DOI
https://doi.org/10.3390/fractalfract9100635
Primary Topic
Complex Systems and Time Series Analysis
Type
article
Field-Weighted Citation Impact
0.00
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article

Stylized Facts of High-Frequency Bitcoin Time Series

Michael Harré, M. N. Najafi, Karina Arias-Calluari, Fernando Alonso-Marroquin et al.
Fractal and Fractional
Complex Systems and Time Series Analysis
article

Stylized Facts of High-Frequency Bitcoin Time Series

Michael Harré, M. N. Najafi, Karina Arias-Calluari, Fernando Alonso-Marroquin, Yaoyue Tang
article en

Abstract

This paper analyzes high-frequency intraday Bitcoin data from 2019 to 2022. The Bitcoin market index exhibits two distinct periods, characterized by abrupt volatility shifts. Bitcoin returns can be described by anomalous diffusion processes, transitioning from subdiffusion for short intervals to weak superdiffusion at longer intervals. Heavy tails are captured well by q-Gaussian distributions, and the autocorrelation of absolute returns shows power law behavior. Both periods display multifractality, with Hurst exponents shifting toward 0.5 over time, indicating increased market efficiency. The time evolution of the empirical PDF of price return allows us to connect these stylized facts to the mathematical framework of multifractals and locally fractional porous medium equations.

Fractal and FractionalVol. 9(10)
The University of Sydney (AU), King Fahd University of Petroleum and Minerals (SA), University of Mohaghegh Ardabili (IR)
Openalex Percentile: Top 65%
Complex Systems and Time Series Analysis
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Stylized Facts of High-Frequency Bitcoin Time Series — Michael Harré, M. N. Najafi, et al. · Fractal and Fractional (2025) | TGRS Research Map | TGRS