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The local Gaussian correlation networks among return tails in the Chinese stock market.

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Title: The local Gaussian correlation networks among return tails in the Chinese stock market.
Authors: Liu 刘鹏, Peng1 (AUTHOR) pengliuhep@outlook.com
Source: International Journal of Modern Physics C: Computational Physics & Physical Computation. Jun2026, Vol. 37 Issue 6, p1-12. 12p.
Subject Terms: *Stocks (Finance), *Financial markets, *Distribution (Probability theory), *Dependence (Statistics), *Investment risk, *Pearson correlation (Statistics)
Company/Entity: Shanghai Stock Exchange
Abstract: Financial networks based on Pearson correlations have been intensively studied. However, previous studies may have led to misleading and catastrophic results because of several critical shortcomings of the Pearson correlation. The local Gaussian correlation coefficient, a new measurement of statistical dependence between variables, has unique advantages including capturing local nonlinear dependence and handling heavy-tailed distributions. This study constructs financial networks using the local Gaussian correlation coefficients between tail regions of stock returns in the Shanghai Stock Exchange. The work systematically analyzes fundamental network metrics including node centrality, average shortest path length and entropy. Compared with the local Gaussian correlation network among positive tails and the conventional Pearson correlation network, the properties of the local Gaussian correlation network among negative tails are more sensitive to the stock market risks. This finding suggests researchers should prioritize the local Gaussian correlation network among negative tails. Future work should reevaluate existing findings using the local Gaussian correlation method. [ABSTRACT FROM AUTHOR]
Database: Academic Search Index
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DbLabel: Academic Search Index
An: 189732913
RelevancyScore: 1452
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 1452.4189453125
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  Data: The local Gaussian correlation networks among return tails in the Chinese stock market.
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  Data: <searchLink fieldCode="AR" term="%22Liu+刘鹏%2C+Peng%22">Liu 刘鹏, Peng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> pengliuhep@outlook.com</i>
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Modern+Physics+C%3A+Computational+Physics+%26+Physical+Computation%22">International Journal of Modern Physics C: Computational Physics & Physical Computation</searchLink>. Jun2026, Vol. 37 Issue 6, p1-12. 12p.
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  Data: *<searchLink fieldCode="DE" term="%22Stocks+%28Finance%29%22">Stocks (Finance)</searchLink><br />*<searchLink fieldCode="DE" term="%22Financial+markets%22">Financial markets</searchLink><br />*<searchLink fieldCode="DE" term="%22Distribution+%28Probability+theory%29%22">Distribution (Probability theory)</searchLink><br />*<searchLink fieldCode="DE" term="%22Dependence+%28Statistics%29%22">Dependence (Statistics)</searchLink><br />*<searchLink fieldCode="DE" term="%22Investment+risk%22">Investment risk</searchLink><br />*<searchLink fieldCode="DE" term="%22Pearson+correlation+%28Statistics%29%22">Pearson correlation (Statistics)</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Shanghai+Stock+Exchange%22">Shanghai Stock Exchange</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Financial networks based on Pearson correlations have been intensively studied. However, previous studies may have led to misleading and catastrophic results because of several critical shortcomings of the Pearson correlation. The local Gaussian correlation coefficient, a new measurement of statistical dependence between variables, has unique advantages including capturing local nonlinear dependence and handling heavy-tailed distributions. This study constructs financial networks using the local Gaussian correlation coefficients between tail regions of stock returns in the Shanghai Stock Exchange. The work systematically analyzes fundamental network metrics including node centrality, average shortest path length and entropy. Compared with the local Gaussian correlation network among positive tails and the conventional Pearson correlation network, the properties of the local Gaussian correlation network among negative tails are more sensitive to the stock market risks. This finding suggests researchers should prioritize the local Gaussian correlation network among negative tails. Future work should reevaluate existing findings using the local Gaussian correlation method. [ABSTRACT FROM AUTHOR]
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1142/S0129183125420070
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 12
        StartPage: 1
    Subjects:
      – SubjectFull: Shanghai Stock Exchange
        Type: general
      – SubjectFull: Stocks (Finance)
        Type: general
      – SubjectFull: Financial markets
        Type: general
      – SubjectFull: Distribution (Probability theory)
        Type: general
      – SubjectFull: Dependence (Statistics)
        Type: general
      – SubjectFull: Investment risk
        Type: general
      – SubjectFull: Pearson correlation (Statistics)
        Type: general
    Titles:
      – TitleFull: The local Gaussian correlation networks among return tails in the Chinese stock market.
        Type: main
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          Name:
            NameFull: Liu 刘鹏, Peng
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          Dates:
            – D: 01
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 01291831
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            – Type: volume
              Value: 37
            – Type: issue
              Value: 6
          Titles:
            – TitleFull: International Journal of Modern Physics C: Computational Physics & Physical Computation
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