Permutation transition entropy: measuring the dynamical complexity of financial time series

DOI10.1016/j.chaos.2020.109962zbMath1490.91150OpenAlexW3036102990MaRDI QIDQ2122934

Xiaojun Zhao, Na Zhang, Mengfan Ji, Pengjian Shang

Publication date: 7 April 2022

Published in: Chaos, Solitons and Fractals (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.chaos.2020.109962

zbMATH Keywords

dynamical complexityfinancial time series analysisMarkov state transitionpermutation transition entropy

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Economic time series analysis (91B84) Measures of information, entropy (94A17)

Related Items

Characterizing the statistical complexity of nonlinear time series via ordinal pattern transition networks ⋮ Measuring market efficiency: the Shannon entropy of high-frequency financial time series ⋮ A new parameter-free entropy based on fragment oscillation and its application in fault diagnosis

Cites Work

• Information entropy as a basic building block of complexity theory
• Permutation entropy of fractional Brownian motion and fractional Gaussian noise
• Analysis of complex time series using refined composite multiscale entropy
• Determining Lyapunov exponents from a time series
• Detrended fluctuation analysis of multivariate time series
• Permutation and weighted-permutation entropy analysis for the complexity of nonlinear time series
• Multiscale transfer entropy: measuring information transfer on multiple time scales
• Permutation entropy revisited
• Measuring the asymmetric contributions of individual subsystems
• Cross-correlations between volume change and price change
• METHODS FOR QUANTIFYING THE CAUSAL STRUCTURE OF BIVARIATE TIME SERIES
• Approximate entropy as a measure of system complexity.
• Markov Chains