MODELLING AND FORECASTING THE VOLATILITY OF THE PORTUGUESE STOCK INDEX PSI-20
Jorge Caiado
Portuguese Journal of Management Studies, 2004, vol. IX, issue 1, 3-21
Abstract:
The volatility clustering often seen in financial data has increased the interest of researchers in applying good models to measure and forecast stock returns. This paper aims to model the volatility for daily and weekly returns of the Portuguese Stock Index PSI-20. By using simple GARCH, GARCH-M, Exponential GARCH (EGARCH) and Threshold ARCH (TARCH) models, we find support that there are significant asymmetric shocks to volatility in the daily stock returns, but not in the weekly stock returns. We also find that some weekly returns time series properties are substantially different from properties of daily returns, and the persistence in conditional volatility is different for some of the sub-periods referred. Finally, we compare the forecasting performance of the various volatility models in the sample periods before and after the terrorist attack on September 11, 2001.
Keywords: EGARCH; forecasting; GARCH; GARCH-M; leverage effect; PSI-20 index; TARCH; volatility. (search for similar items in EconPapers)
Date: 2004
References: Add references at CitEc
Citations: View citations in EconPapers (5)
Downloads: (external link)
https://ejms.iseg.ulisboa.pt/files/2004-Modelling_ ... ock_index_PSI-20.pdf (application/pdf)
Related works:
Working Paper: Modelling and forecasting the volatility of the portuguese stock index PSI-20 (2004) 
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:pjm:journl:v:ix:y:2004:i:1:p:3-21
Access Statistics for this article
Portuguese Journal of Management Studies is currently edited by Luís Mota de Castro, Tiago Cardão-Pito, Mark Crathorne
More articles in Portuguese Journal of Management Studies from ISEG, Universidade de Lisboa Contact information at EDIRC.
Bibliographic data for series maintained by Luís Mota de Castro, Tiago Cardão-Pito, Mark Crathorne ().