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

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Author Info

  • Taisei Kaizoji
  • Thomas Lux

Abstract

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

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Bibliographic Info

Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2004 with number 158.

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Date of creation: 11 Aug 2004
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Handle: RePEc:sce:scecf4:158

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Web page: http://comp-econ.org/
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Keywords: long memory models; volume; volatility;

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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.

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