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

Author

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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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    1. Diks Cees & Panchenko Valentyn, 2005. "A Note on the Hiemstra-Jones Test for Granger Non-causality," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 9(2), pages 1-9, June.
    2. Yuan, Ying & Zhuang, Xin-tian & Liu, Zhi-ying, 2012. "Price–volume multifractal analysis and its application in Chinese stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(12), pages 3484-3495.
    3. M. F. M. Osborne, 1959. "Brownian Motion in the Stock Market," Operations Research, INFORMS, vol. 7(2), pages 145-173, April.
    4. Barron, Orie E. & Karpoff, Jonathan M., 2004. "Information precision, transaction costs, and trading volume," Journal of Banking & Finance, Elsevier, vol. 28(6), pages 1207-1223, June.
    5. Zunino, Luciano & Zanin, Massimiliano & Tabak, Benjamin M. & Pérez, Darío G. & Rosso, Osvaldo A., 2010. "Complexity-entropy causality plane: A useful approach to quantify the stock market inefficiency," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(9), pages 1891-1901.
    6. Dufour, Jean-Marie & Taamouti, Abderrahim, 2010. "Short and long run causality measures: Theory and inference," Journal of Econometrics, Elsevier, vol. 154(1), pages 42-58, January.
    7. Karpoff, Jonathan M., 1987. "The Relation between Price Changes and Trading Volume: A Survey," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 22(1), pages 109-126, March.
    8. Diks, Cees & Panchenko, Valentyn, 2006. "A new statistic and practical guidelines for nonparametric Granger causality testing," Journal of Economic Dynamics and Control, Elsevier, vol. 30(9-10), pages 1647-1669.
    9. Mariano Matilla‐García & José Miguel Rodríguez & Manuel Ruiz Marín, 2010. "A symbolic test for testing independence between time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 31(2), pages 76-85, March.
    10. Hiemstra, Craig & Jones, Jonathan D, 1994. "Testing for Linear and Nonlinear Granger Causality in the Stock Price-Volume Relation," Journal of Finance, American Finance Association, vol. 49(5), pages 1639-1664, December.
    11. Dimitris Politis & Halbert White, 2004. "Automatic Block-Length Selection for the Dependent Bootstrap," Econometric Reviews, Taylor & Francis Journals, vol. 23(1), pages 53-70.
    12. Matilla-García, Mariano & Ruiz Marín, Manuel, 2009. "Detection of non-linear structure in time series," Economics Letters, Elsevier, vol. 105(1), pages 1-6, October.
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    Cited by:

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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.
    10. Zhang, Xin & Yang, Liansheng & Zhu, Yingming, 2019. "Analysis of multifractal characterization of Bitcoin market based on multifractal detrended fluctuation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 973-983.
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