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A Copula-Based Correlation Measure And Its Application In Chinese Stock Market

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  • FENGHUA WEN

    (School of Economics Changsha University of Science & Technology Changsha, Hunan Province, 410076, P. R. China)

  • ZHIFENG LIU

    (School of Economics Changsha University of Science & Technology Changsha, Hunan Province, 410076, P. R. China)

Abstract

In this paper, a copula-based correlation measure is proposed to test the interdependence among stochastic variables in terms of copula function. Based on a geometric analysis of copula function, a new derivation method is introduced to derive the Gini correlation coefficient. Meantime theoretical analysis finds that the Gini correlation coefficient tends to overestimate the tail interdependence in the case of stochastic variables clustering at the tails. For this overestimation issue, a fully new correlation coefficient called Co is developed and extended to measure the tail interdependence. Empirical study shows that the new correlation coefficient Co can effectively solve the overestimation issue, which implies that the proposed new correlation coefficient is more suitable to describe the interdependence among stochastic variables than the Gini correlation coefficient.

Suggested Citation

  • Fenghua Wen & Zhifeng Liu, 2009. "A Copula-Based Correlation Measure And Its Application In Chinese Stock Market," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 8(04), pages 787-801.
    • Handle: RePEc:wsi:ijitdm:v:08:y:2009:i:04:n:s0219622009003612
    DOI: 10.1142/S0219622009003612
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    Citations

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    Cited by:

    1. Gang-Jin Wang & Chi Xie & Peng Zhang & Feng Han & Shou Chen, 2014. "Dynamics of Foreign Exchange Networks: A Time-Varying Copula Approach," Discrete Dynamics in Nature and Society, Hindawi, vol. 2014, pages 1-11, May.
    2. Yiding Yue & Jinyou Zou, 2014. "The Role of Wealth and Health in Insurance Choice: Bivariate Probit Analysis in China," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-9, March.
    3. Ma, Pengcheng & Li, Daye & Li, Shuo, 2016. "Efficiency and cross-correlation in equity market during global financial crisis: Evidence from China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 163-176.
    4. Umar, Zaghum & Usman, Muhammad & Choi, Sun-Yong & Rice, John, 2023. "Diversification benefits of NFTs for conventional asset investors: Evidence from CoVaR with higher moments and optimal hedge ratios," Research in International Business and Finance, Elsevier, vol. 65(C).
    5. Nasibeh Mollahasani, 2024. "A Hybrid Spectral-Finite Difference Method for Numerical Pricing of Time-Fractional Black–Scholes Equation," Computational Economics, Springer;Society for Computational Economics, vol. 64(2), pages 841-869, August.
    6. Nuugulu, Samuel M & Gideon, Frednard & Patidar, Kailash C, 2021. "A robust numerical scheme for a time-fractional Black-Scholes partial differential equation describing stock exchange dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 145(C).

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