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Does trading volume really explain stock returns volatility?

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  • Thierry Ané

    ()
    (University of Reims, IÉSEG School of Management)

  • Loredana Ureche-Rangau

    ()
    (IÉSEG School of Management)

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 (MDH) represents an intuitive and appealing explanation for the empirically observed correlation between volume and volatility of speculative assets. 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 and then decreases for other order moments while the differencing parameter of the trading volume remains remarkably unchanged. 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.

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Bibliographic Info

Paper provided by IESEG School of Management in its series Working Papers with number 2004-FIN-02.

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Length: 36 pages
Date of creation: Jul 2004
Date of revision:
Publication status: Published in Journal of International Financial Markets Institutions and Money, July 2008, 18, pp. 216-235
Handle: RePEc:ies:wpaper:f200402

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Keywords: Volatility Persistence; Long Memory; Trading Volume;

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References

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  1. Tauchen, George E & Pitts, Mark, 1983. "The Price Variability-Volume Relationship on Speculative Markets," Econometrica, Econometric Society, vol. 51(2), pages 485-505, March.
  2. Lobato, I. & Robinson, P. M., 1996. "Averaged periodogram estimation of long memory," Journal of Econometrics, Elsevier, vol. 73(1), pages 303-324, July.
  3. Andersen, Torben G, 1996. " Return Volatility and Trading Volume: An Information Flow Interpretation of Stochastic Volatility," Journal of Finance, American Finance Association, vol. 51(1), pages 169-204, March.
  4. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
  5. Clark, Peter K, 1973. "A Subordinated Stochastic Process Model with Finite Variance for Speculative Prices," Econometrica, Econometric Society, vol. 41(1), pages 135-55, January.
  6. Lamoureux, Christopher G & Lastrapes, William D, 1990. " Heteroskedasticity in Stock Return Data: Volume versus GARCH Effects," Journal of Finance, American Finance Association, vol. 45(1), pages 221-29, March.
  7. Watanabe, Toshiaki, 2000. "Bayesian Analysis of Dynamic Bivariate Mixture Models: Can They Explain the Behavior of Returns and Trading Volume?," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(2), pages 199-210, April.
  8. Lobato, Ignacio N & Velasco, Carlos, 2000. "Long Memory in Stock-Market Trading Volume," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(4), pages 410-27, October.
  9. Liesenfeld, Roman, 1998. "Dynamic Bivariate Mixture Models: Modeling the Behavior of Prices and Trading Volume," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(1), pages 101-09, January.
  10. Bollerslev, Tim & Jubinski, Dan, 1999. "Equity Trading Volume and Volatility: Latent Information Arrivals and Common Long-Run Dependencies," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(1), pages 9-21, January.
  11. Liesenfeld, Roman, 2001. "A generalized bivariate mixture model for stock price volatility and trading volume," Journal of Econometrics, Elsevier, vol. 104(1), pages 141-178, August.
  12. Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June.
  13. Harris, Lawrence, 1986. "Cross-Security Tests of the Mixture of Distributions Hypothesis," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 21(01), pages 39-46, March.
  14. Granger, Clive W. J. & Ding, Zhuanxin, 1996. "Varieties of long memory models," Journal of Econometrics, Elsevier, vol. 73(1), pages 61-77, July.
  15. Harris, Lawrence, 1987. "Transaction Data Tests of the Mixture of Distributions Hypothesis," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 22(02), pages 127-141, June.
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Citations

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Cited by:
  1. 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.
  2. 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.
  3. 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.
  4. Christian, Mueller-Kademann, 2009. "Puzzle solver," MPRA Paper 19852, University Library of Munich, Germany.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. Fernandez, Viviana, 2010. "Commodity futures and market efficiency: A fractional integrated approach," Resources Policy, Elsevier, vol. 35(4), pages 276-282, December.
  10. Kumar, Brajesh & Singh, Priyanka & Pandey, Ajay, . "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.

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