Forecasting volatility in the financial markets: a comparison of alternative distributional assumptions

I.-Yuan Chuang, Jin-Ray Lu and Pei-Hsuan Lee

Applied Financial Economics, 2007, vol. 17, issue 13, 1051-1060

Abstract: This article analyses the volatility forecasting performance of the GARCH models based on various distributional assumptions in the context of stock market indices and exchange rate returns. Using rollover methods to construct the out-of-the-sample volatility forecasts, this study shows that the GARCH model combined with the logistic distribution, the scaled student's t distribution and the Riskmetrics model are preferable both in stock markets and foreign exchange markets. The exponential power and the mixture of two normal distributions are, however, less recommended. Furthermore, a complex distribution does not always outperform a simpler one, although the exact ranking depends on the application of underlying assets and the performance statistics being used.

Date: 2007
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

Downloads: (external link)
http://www.tandfonline.com/doi/abs/10.1080/09603100600771000 (text/html)
Access to full text is restricted to subscribers.

Related works:
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:taf:apfiec:v:17:y:2007:i:13:p:1051-1060

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RAFE20

DOI: 10.1080/09603100600771000

Access Statistics for this article

Applied Financial Economics is currently edited by Anita Phillips

More articles in Applied Financial Economics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().