Arbeitspapier
Forecasting volatility and volume in the Tokyo stock market: The advantage of long memory models
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.
- Sprache
-
Englisch
- Erschienen in
-
Series: Economics Working Paper ; No. 2004-05
- Klassifikation
-
Wirtschaft
Asset Pricing; Trading Volume; Bond Interest Rates
Forecasting Models; Simulation Methods
Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- Thema
-
Long memory models
Volume
Volatility
Forecasting
Börsenkurs
Volatilität
Börsenumsatz
Prognoseverfahren
Zeitreihenanalyse
Schätzung
Aktienmarkt
Japan
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Lux, Thomas
Kaizoji, Taisei
- Ereignis
-
Veröffentlichung
- (wer)
-
Kiel University, Department of Economics
- (wo)
-
Kiel
- (wann)
-
2004
- Handle
- Letzte Aktualisierung
-
03.04.2025, 12:45 MESZ
Datenpartner
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ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Lux, Thomas
- Kaizoji, Taisei
- Kiel University, Department of Economics
Entstanden
- 2004
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