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Title:Forecasting volatility and volume in the Tokyo stock market: The advantage of long memory models PDF Logo
Authors:Lux, Thomas
Kaizoji, Taisei
Issue Date:2004
Series/Report no.:Economics working paper / Christian-Albrechts-Universität Kiel, Department of Economics 2004,05
Abstract:We investigate the predictability of both volatility and volume for a large sample of Japanese stocks. The particular emphasis of this paper is on assessing 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 the recently introduced multifractal models) dominate over GARCH and ARMA models. However, while FIGARCH and ARFIMA also have a number of cases with dramatic failures of their forecasts, the multifractal model does not suffer from this shortcoming and its performance practically always improves upon the na?ve forecast provided by historical volatility. As a somewhat surprising result, we also find that, for FIGARCH and ARFIMA models, pooled estimates (i.e. averages of parameter estimates from a sample of time series) give much better results than individually estimated models.
Subjects:Forecasting
Long memory models
Volume
Volatility
JEL:G12
C53
C22
Document Type:Working Paper
Appears in Collections:Publikationen von Forscherinnen und Forschern des IfW
Economics Working Papers, Department of Economics, CAU Kiel

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