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Surprise Volume and Heteroskedasticity in Equity Market Returns


  • Niklas Wagner
  • Terry A. Marsh


Heteroskedasticity in returns may be explainable by trading volume. We use different volume variables, including surprise volume---i.e. unexpected above-average trading activity---which is derived from uncorrelated volume innovations. Assuming weakly exogenous volume, we extend the Lamoureux and Lastrapes (1990) model by an asymmetric GARCH in-mean specification following Golsten et al. (1993). Model estimation for the U.S. as well as six large equity markets shows that surprise volume provides superior model fit and helps to explain volatility persistence as well as excess kurtosis. Surprise volume reveals a significant positive market risk premium, asymmetry, and a surprise volume effect in conditional variance. The findings suggest that, e.g., a surprise volume shock (breakdown)---i.e. large (small) contemporaneous and small (large) lagged surprise volume---relates to increased (decreased) conditional market variance and return.

Suggested Citation

  • Niklas Wagner & Terry A. Marsh, 2004. "Surprise Volume and Heteroskedasticity in Equity Market Returns," Econometrics 0409009, EconWPA.
  • Handle: RePEc:wpa:wuwpem:0409009
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    References listed on IDEAS

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    Cited by:

    1. Heejoon Han & Dennis Kristensen, 2014. "Asymptotic Theory for the QMLE in GARCH-X Models With Stationary and Nonstationary Covariates," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(3), pages 416-429, July.
    2. José Dias Curto & João Tomaz & José Castro Pinto, 2009. "A new approach to bad news effects on volatility: the multiple-sign-volume sensitive regime EGARCH model (MSV-EGARCH)," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 8(1), pages 23-36, April.
    3. Jianxin Wang, 2007. "Foreign Ownership and Volatility Dynamics of Indonesian Stocks," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 14(3), pages 201-210, September.
    4. Zhang, Yongjie & Feng, Lina & Jin, Xi & Shen, Dehua & Xiong, Xiong & Zhang, Wei, 2014. "Internet information arrival and volatility of SME PRICE INDEX," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 399(C), pages 70-74.
    5. 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.

    More about this item


    ARCH; trading volume; return volume dependence; asymmetric volatility; market risk premium; leverage effect;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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