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Forecasting Volume and Volatility in the Tokyo Stock Market: The Advantage of Long Memory Models


  • Taisei Kaizoji
  • Thomas Lux


We investigate the predictability of both volatility and volume for a large sample of Japanese stocks. The particular emphasis of this paper is an assessment of the performance of long memory time series models in comparison to their short-memory counterparts. Since long memory models should have a particular advantage over long forecasting horizons, we consider predictions of up to 100 days ahead. In most respects, the long memory models (ARFIMA, FIGARCH and multifractal models) dominate over GARCH and ARMA models. As a somewhat surprising result, we find that, for FIGARCH and ARFIMA models, pooled estimates (i.e. averages of parameter estimates from a sample of time series) give vastly better results than individually estimated models

Suggested Citation

  • Taisei Kaizoji & Thomas Lux, 2004. "Forecasting Volume and Volatility in the Tokyo Stock Market: The Advantage of Long Memory Models," Computing in Economics and Finance 2004 158, Society for Computational Economics.
  • Handle: RePEc:sce:scecf4:158

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    References listed on IDEAS

    1. Bernheim, B Douglas, 1994. "A Theory of Conformity," Journal of Political Economy, University of Chicago Press, vol. 102(5), pages 841-877, October.
    2. Abhijit V. Banerjee, 1992. "A Simple Model of Herd Behavior," The Quarterly Journal of Economics, Oxford University Press, vol. 107(3), pages 797-817.
    3. Brian Krauth, 2006. "Social interactions in small groups," Canadian Journal of Economics, Canadian Economics Association, vol. 39(2), pages 414-433, May.
    4. Milgrom, Paul & Roberts, John, 1990. "Rationalizability, Learning, and Equilibrium in Games with Strategic Complementarities," Econometrica, Econometric Society, vol. 58(6), pages 1255-1277, November.
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    Cited by:

    1. Chortareas, Georgios & Jiang, Ying & Nankervis, John. C., 2011. "Forecasting exchange rate volatility using high-frequency data: Is the euro different?," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1089-1107, October.
    2. Georgios Chortareas & John Nankervis & Ying Jiang, 2007. "Forecasting Exchange Rate Volatility with High Frequency Data: Is the Euro Different?," Money Macro and Finance (MMF) Research Group Conference 2006 79, Money Macro and Finance Research Group.

    More about this item


    long memory models; volume; volatility;

    JEL classification:

    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates


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