Complexity of Financial Time Series: Multifractal and Multiscale Entropy Analyses
Statistical Finance
2025-08-01 v1 Data Analysis, Statistics and Probability
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.
Keywords
Cite
@article{arxiv.2507.23414,
title = {Complexity of Financial Time Series: Multifractal and Multiscale Entropy Analyses},
author = {Oday Masoudi and Farhad Shahbazi and Mohammad Sharifi},
journal= {arXiv preprint arXiv:2507.23414},
year = {2025}
}