Skip to content
All library documents

Comparing Multifractal and Entropy Complexity Across Markets

Article arXiv papers · Author: Oday Masoudi et al.
Read the original

Summary

The study compares the complexity of log-return series for Bitcoin, GBP/USD, gold, and natural gas. It applies Multifractal Detrended Fluctuation Analysis and Refined Composite Multiscale Sample Entropy, two approaches that quantify different aspects of structure across scales. The comparison is intended to inform how market predictability and associated risks may differ across asset classes.

Both measures indicate greater complexity in Bitcoin returns than in the other series. The authors suggest that stronger nonlinear correlations in Bitcoin may help explain this result. The description provides no sample dates, parameter choices, uncertainty estimates, or trading tests, so the finding should be treated as a comparative time-series result rather than proof that Bitcoin is less predictable in every setting. Complexity measures characterize return structure; the excerpt does not establish how they translate into forecast accuracy or investment performance.

Key ideas

  • The study compares return complexity across Bitcoin, GBP/USD, gold, and natural gas.
  • MF-DFA measures multifractal structure in return series.
  • RCMSE assesses sample entropy across multiple time scales.
  • Both measures rank Bitcoin returns as more complex than the other studied markets.
  • The proposed explanation is stronger nonlinear correlation, but no direct trading-performance test is described.

Tags

CryptoForexCommoditiesStatisticsVolatility
Full text
# Complexity of Financial Time Series: Multifractal and Multiscale Entropy Analyses


# Complexity of Financial Time Series: Multifractal and Multiscale Entropy Analyses









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.

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.