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http://dspace.mediu.edu.my:8181/xmlui/handle/10419/3244| Title: | Forecasting volatility and volume in the Tokyo stock market: The advantage of long memory models |
| Keywords: | G12 C53 C22 ddc:330 Forecasting Long memory models Volume Volatility Börsenkurs Volatilität Börsenumsatz Prognoseverfahren Zeitreihenanalyse Schätzung Aktienmarkt Japan |
| Issue Date: | 16-Oct-2013 |
| Publisher: | Institut für Volkswirtschaftslehre, Kiel |
| 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. |
| URI: | http://koha.mediu.edu.my:8181/xmlui/handle/10419/3244 |
| Other Identifiers: | http://hdl.handle.net/10419/3244 ppn:388943122 RePEc:zbw:cauewp:1936 |
| Appears in Collections: | EconStor |
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