Generalized Dynamic Factor Model + GARCH Exploiting Multivariate Information for Univariate Prediction

Lucia Alessi, Matteo Barigozzi and Marco Capasso

LEM Papers Series from Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy

Abstract: We propose a new model for multivariate forecasting which combines the Generalized Dynamic Factor Model (GDFM)and the GARCH model. The GDFM, applied to a huge number of series, captures the multivariate information and disentangles the common and the idiosyncratic part of each series of returns. In this financial analysis, both these components are modeled as a GARCH. We compare GDFM+GARCH and standard GARCH performance on samples up to 475 series, predicting both levels and volatility of returns. While results on levels are not significantly different, on volatility the GDFM+GARCH model outperforms the standard GARCH in most cases. These results are robust with respect to different volatility proxies.

Keywords: Dynamic Factors; GARCH; Volatility Forecasting (search for similar items in EconPapers)
Date: 2006-05-13
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-fin, nep-fmk and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://www.lem.sssup.it/WPLem/files/2006-13.pdf (application/pdf)

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:ssa:lemwps:2006/13

Access Statistics for this paper

More papers in LEM Papers Series from Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy Contact information at EDIRC.
Bibliographic data for series maintained by ( this e-mail address is bad, please contact ).