Forecasting Volume and Volatility in the Tokyo Stock Market: The Advantage of Long Memory Models
Taisei Kaizoji () and
No 158, Computing in Economics and Finance 2004 from Society for Computational Economics
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
Keywords: long memory models; volume; volatility (search for similar items in EconPapers)
JEL-codes: C20 G12 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:sce:scecf4:158
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