Complexity of Financial Time Series: Multifractal and Multiscale Entropy Analyses

2025/07/31 by Oday Masoudi, Masoudi, Oday, Farhad Shahbazi +3
Economics, Econometrics and Finance · #Complex Systems and Time Series Analysis #Data Analysis #FOS: Economics and business #FOS: Physical sciences #Statistical Finance (q-fin.ST) #Statistics and Probability (physics.data-an)

paper · pdf · doi:10.48550/arxiv.2507.23414

openalex publication_date 2025/07/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We employed Multifractal Detrended Fluctuation Analysis (MF-DFA) and Refined Composite Multiscale Sample Entropy (RCMSE) to investigate the complexity of Bitcoin, GBP/USD, gold, and natural gas price log-return time series. This study provides a comparative analysis of these markets and offers insights into their predictability and associated risks. Each tool presents a unique method to quantify time series complexity. The RCMSE and MF-DFA methods demonstrate a higher complexity for the Bitcoin time series than others. It is discussed that the increased complexity of Bitcoin may be attributable to the presence of higher nonlinear correlations within its log-return time series.

Citations

• Physiological time-series analysis using approximate entropy and sample entropy
• Mosaic organization of DNA nucleotides

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