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Does trading volume really explain stock returns volatility?

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  • Ané, Thierry
  • Ureche-Rangau, Loredana

Abstract

Assuming that the variance of daily price changes and trading volume are both driven by the same latent variable measuring the number of price-relevant information arriving on the market, the mixture of distribution hypothesis represents an intuitive and appealing explanation for the empirically observed correlation between volume and volatility. This paper investigates to which extent the temporal dependence of volatility and volume is compatible with a MDH model through a systematic analysis of the long memory properties of power transformations of both series. It is found that the fractional differencing parameter of the volatility series reaches its maximum for a power transformation around 0.75 and then decreases for other order moments while the differencing parameter of the trading volume remains remarkably unchanged. Similarly, the generalized Hurst exponent of the volatility series appears to be a concave function of the power transformation, indicating the presence of a multi-fractal process, while it remains constant for the trading volume, revealing its uni-fractal structure. The volatility process thus exhibits a high degree of intermittence whereas the volume dynamic appears much smoother. The results suggest that volatility and volume may share common short-term movements but that their long-run behavior is fundamentally different.

Suggested Citation

  • Ané, Thierry & Ureche-Rangau, Loredana, 2008. "Does trading volume really explain stock returns volatility?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 18(3), pages 216-235, July.
  • Handle: RePEc:eee:intfin:v:18:y:2008:i:3:p:216-235
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    Cited by:

    1. Onali, Enrico & Goddard, John, 2009. "Unifractality and multifractality in the Italian stock market," International Review of Financial Analysis, Elsevier, vol. 18(4), pages 154-163, September.
    2. Esqueda, Omar A. & Assefa, Tibebe A. & Mollick, André Varella, 2012. "Financial globalization and stock market risk," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 22(1), pages 87-102.
    3. Müller, Christian, 2015. "Radical uncertainty: Sources, manifestations and implications," Economics Discussion Papers 2015-41, Kiel Institute for the World Economy (IfW).
    4. Fernandez Viviana, 2011. "Alternative Estimators of Long-Range Dependence," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 15(2), pages 1-37, March.
    5. Fernandez, Viviana, 2009. "The behavior of stock returns in the mining industry following the Iraq war," Research in International Business and Finance, Elsevier, vol. 23(3), pages 274-292, September.
    6. Christian, Mueller-Kademann, 2009. "Puzzle solver," MPRA Paper 19852, University Library of Munich, Germany.
    7. Fernandez, Viviana, 2010. "Commodity futures and market efficiency: A fractional integrated approach," Resources Policy, Elsevier, vol. 35(4), pages 276-282, December.
    8. Rodrigo F. Aranda L. & Patricio Jaramillo G., 2010. "Non-linear Dynamics in the Chilean Stock Market: Evidence on Traded Volumes and Returns," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 13(3), pages 67-94, December.
    9. Kumar, Brajesh & Singh, Priyanka & Pandey, Ajay, 2009. "The Dynamic Relationship between Price and Trading Volume:Evidence from Indian Stock Market," IIMA Working Papers WP2009-12-04, Indian Institute of Management Ahmedabad, Research and Publication Department.
    10. Viviana Fernández, 2007. "The behavior of stock returns in the Asia-Pacific mining industry following the Iraq war," Documentos de Trabajo 243, Centro de Economía Aplicada, Universidad de Chile.
    11. Carroll, Rachael & Kearney, Colm, 2015. "Testing the mixture of distributions hypothesis on target stocks," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 39(C), pages 1-14.
    12. Brajesh Kumar & Priyanka Singh & Ajay Pandey, 2010. "The Dynamic Relationship between Price and Trading Volume: Evidence from Indian Stock Market," Working Papers id:2379, eSocialSciences.
    13. Loredana Ureche-Rangau & Fabien Collado & Ulysse Galiay, 2011. "The dynamics of the volatility – trading volume relationship: New evidence from developed and emerging markets," Economics Bulletin, AccessEcon, vol. 31(3), pages 2569-2583.
    14. Jawadi Fredj & Ureche-Rangau Loredana, 2013. "Threshold linkages between volatility and trading volume: evidence from developed and emerging markets," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(3), pages 313-333, May.

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    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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