Weighted multiscale cumulative residual Rényi permutation entropy of financial time series

Qin Zhou and Pengjian Shang

Physica A: Statistical Mechanics and its Applications, 2020, vol. 540, issue C

Abstract: In this paper, based on cumulative residual entropy (CRE) and Rényi permutation entropy (RPE), multiscale cumulative residual Rényi permutation entropy (MCRRRPE) and weighted multiscale cumulative residual Rényi permutation entropy (WMCRRPE) are proposed as novelty measures to quantify the uncertainty of the nonlinear time series. First, the MCRRPE and WMCRRPE methods are performed on the synthetic data, and the impact of the changes to parameters is discussed. Then, the MCRRPE and WMCRRPE methods are applied to the closing prices of the US, European and Chinese stock markets. We analyze the statistics and draw some conclusions from the comparison: the stock markets can be divided into four categories: (1) NASDAQ and FTSE100, (2) S&P500, DJI and HSI, (3) DAX30 and CAC40 and (4) SSE and SZSE. The standard deviation has a certain relationship with WMCRRPE. Compared with the MCRRPE method, the WMCRRPE method can distinguish these financial stock markets effectively and better estimate the complexity of the time series containing amplitude information.

Keywords: Cumulative residual Rényi permutation entropy; Weight; Multiple scales; Financial time series (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:540:y:2020:i:c:s037843711931742x

DOI: 10.1016/j.physa.2019.123089

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