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Research on weighted Havrda–Charvat’s entropy in financial time series

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  • Shi, Yetian
  • Wu, Yue
  • Shang, Pengjian

Abstract

In this paper, based on permutation patterns and Havrda–Charvat’s entropy, we propose a new measurement of complexity, weighted Havrda–Charvat’s entropy, to distinguish uncertainty for time series with the same order mode. First, we use logistic mapping to analyze the effect of similar data, length, noise, and weight on Havrda–Charvat’s entropy. Then we apply Havrda–Charvat’s entropy and weighted Havrda–Charvat’s entropy to stock market data including Shanghai Composite Index, CSI 300 index, Shenzhen Component Index, S&P500, DOW30 and NASDAQ. Results show that Havrda–Charvat’s entropy is helpful for distinguishing the uncertainty between similar data and it is robust to noise. Besides, the weighted Havrda–Charvat’s entropy values are of less deviation so as to make the analysis of complexity more convincing.

Suggested Citation

  • Shi, Yetian & Wu, Yue & Shang, Pengjian, 2021. "Research on weighted Havrda–Charvat’s entropy in financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 572(C).
    • Handle: RePEc:eee:phsmap:v:572:y:2021:i:c:s0378437121001862
    DOI: 10.1016/j.physa.2021.125914
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    References listed on IDEAS

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    1. Satish Kumar & Gurdas Ram, 2014. "A Generalization of the Havrda-Charvat and Tsallis Entropy and Its Axiomatic Characterization," Abstract and Applied Analysis, Hindawi, vol. 2014, pages 1-8, February.
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