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Forecasting volatility and volume in the Tokyo stock market: The advantage of long memory models

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dc.creator Lux, Thomas
dc.creator Kaizoji, Taisei
dc.date 2004
dc.date.accessioned 2013-10-16T06:19:24Z
dc.date.available 2013-10-16T06:19:24Z
dc.date.issued 2013-10-16
dc.identifier http://hdl.handle.net/10419/3244
dc.identifier ppn:388943122
dc.identifier RePEc:zbw:cauewp:1936
dc.identifier.uri http://koha.mediu.edu.my:8181/xmlui/handle/10419/3244
dc.description 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.
dc.language eng
dc.publisher Institut für Volkswirtschaftslehre, Kiel
dc.relation Economics working paper / Christian-Albrechts-Universität Kiel, Department of Economics 2004,05
dc.rights http://www.econstor.eu/dspace/Nutzungsbedingungen
dc.subject G12
dc.subject C53
dc.subject C22
dc.subject ddc:330
dc.subject Forecasting
dc.subject Long memory models
dc.subject Volume
dc.subject Volatility
dc.subject Börsenkurs
dc.subject Volatilität
dc.subject Börsenumsatz
dc.subject Prognoseverfahren
dc.subject Zeitreihenanalyse
dc.subject Schätzung
dc.subject Aktienmarkt
dc.subject Japan
dc.title Forecasting volatility and volume in the Tokyo stock market: The advantage of long memory models
dc.type doc-type:workingPaper


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