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A permutation entropy based test for causality: The volume–stock price relation

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  • Matilla-García, Mariano
  • Marín, Manuel Ruiz
  • Dore, Mohammed I.

Abstract

The purpose of this paper is to propose a newly developed non-parametric test for linear and nonlinear causality based on permutation entropy and to show its usefulness in analyzing the potential causal relationship between trading volume and security prices. Most of the empirical applications and tests for causality rely on using Granger causality based test for linear models. Although these tests have high power in uncovering linear causal relations, their power against nonlinear causal relations can be low. Our test is designed to deal with the detection of linear and non-linear causality. We also compare our permutation entropy based test with other Granger causality tests. Monte Carlo simulations show excellent performance (in terms of size and power) of the new test for detecting linear and non-linear causality under different scenarios. Our conclusions point that there is a bidirectional causal relation from volume to price returns not only in the mean but also in the variance.

Suggested Citation

  • Matilla-García, Mariano & Marín, Manuel Ruiz & Dore, Mohammed I., 2014. "A permutation entropy based test for causality: The volume–stock price relation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 398(C), pages 280-288.
    • Handle: RePEc:eee:phsmap:v:398:y:2014:i:c:p:280-288
    DOI: 10.1016/j.physa.2013.11.031
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    2. Ashok Chanabasangouda Patil & Shailesh Rastogi, 2019. "Time-Varying Price–Volume Relationship and Adaptive Market Efficiency: A Survey of the Empirical Literature," JRFM, MDPI, vol. 12(2), pages 1-18, June.
    3. Weiß, Christian H. & Ruiz Marín, Manuel & Keller, Karsten & Matilla-García, Mariano, 2022. "Non-parametric analysis of serial dependence in time series using ordinal patterns," Computational Statistics & Data Analysis, Elsevier, vol. 168(C).
    4. Wang, Qizhen & Zhu, Yingming & Yang, Liansheng & Mul, Remco A.H., 2017. "Coupling detrended fluctuation analysis of Asian stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 337-350.
    5. Wang, Lu & Ruan, Hang & Hong, Yanran & Luo, Keyu, 2023. "Detecting the hidden asymmetric relationship between crude oil and the US dollar: A novel neural Granger causality method," Research in International Business and Finance, Elsevier, vol. 64(C).
    6. Panpan Wang & Tsungwu Ho & Yishi Li, 2020. "The Price-Volume Relationship of the Shanghai Stock Index: Structural Change and the Threshold Effect of Volatility," Sustainability, MDPI, vol. 12(8), pages 1-17, April.
    7. Gu, Rongbao & Xiong, Wei & Li, Xinjie, 2015. "Does the singular value decomposition entropy have predictive power for stock market? — Evidence from the Shenzhen stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 439(C), pages 103-113.
    8. Camacho, Maximo & Romeu, Andres & Ruiz-Marin, Manuel, 2021. "Symbolic transfer entropy test for causality in longitudinal data," Economic Modelling, Elsevier, vol. 94(C), pages 649-661.
    9. Angeliki Papana & Catherine Kyrtsou & Dimitris Kugiumtzis & Cees Diks, 2023. "Identification of causal relationships in non-stationary time series with an information measure: Evidence for simulated and financial data," Empirical Economics, Springer, vol. 64(3), pages 1399-1420, March.
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