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Does trading volume really explain stock returns volatility?

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Author Info
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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Publisher 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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Related research
Keywords: Volatility Persistence; Long Memory; Trading Volume;

Find related papers by JEL classification:
C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation
C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation and Testing
G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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    Other versions:
  2. 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. [Downloadable!] (restricted)
  3. Lobato, I. & Robinson, P. M., 1996. "Averaged periodogram estimation of long memory," Journal of Econometrics, Elsevier, vol. 73(1), pages 303-324, July. [Downloadable!] (restricted)
  4. 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. [Downloadable!] (restricted)
  5. 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. [Downloadable!]
  6. 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.
  7. 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.
  8. Sharma, Jandhyala L & Moughoue, Mbodja & Kamath, Ravindra, 1996. "Heteroscedasticity in Stock Market Indicator Return Data: Volume versus GARCH Effects," Applied Financial Economics, Taylor and Francis Journals, vol. 6(4), pages 337-42, August. [Downloadable!] (restricted)
  9. 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. [Downloadable!] (restricted)
    Other versions:
  10. 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.
  11. 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. [Downloadable!] (restricted)
  12. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April. [Downloadable!] (restricted)
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  14. 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. [Downloadable!]
  15. 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. [Downloadable!] (restricted)
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