@techreport{Lux2006Forecasting,
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 model) dominate over GARCH and ARMA models. However, while
FIGARCH and ARFIMA also have quite 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.},
author = {Thomas Lux and Taisei Kaizoji},
copyright = {http://www.econstor.eu/dspace/Nutzungsbedingungen},
editor = {Institut f\"{u}r Volkswirtschaftslehre, Kiel},
keywords = {C53; G12; C22; 330; Forecasting; Long memory models; Volume; Volatility; B\"{o}rsenkurs; Volatilit\"{a}t; B\"{o}rsenumsatz; Prognoseverfahren; Zeitreihenanalyse; Sch\"{a}tzung; Aktienmarkt; Japan},
language = {eng},
number = {2006,13; Download aus dem Internet, Stand: 04.12.2006; 2006,13},
publisher = {Institut f\"{u}r Volkswirtschaftslehre, Kiel},
series = {Economics working paper},
title = {Forecasting volatility and volume in the Tokyo stock market: Long memory, fractality and regime switching},
type = {Economics working paper / Christian-Albrechts-Universit\"{a}t Kiel, Department of Economics},
url = {http://hdl.handle.net/10419/3924},
year = {2006}
}
