Generalized dynamic factor models and volatilities estimation and forecasting
Matteo Barigozzi and
Marc Hallin ()
LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library
In large panels of financial time series with dynamic factor structure on the levels or returns, the volatilities of the common and idiosyncratic components often exhibit strong correlations, indicating that both are exposed to the same market volatility shocks. This suggests, alongside the dynamic factor decomposition of returns, a dynamic factor decomposition of volatilities or volatility proxies. Based on this observation, Barigozzi and Hallin (2016) proposed an entirely non-parametric and model-free two-step general dynamic factor approach which accounts for a joint factor structure of returns and volatilities, and allows for extracting the market volatility shocks. Here, we go one step further, and show how the same two-step approach naturally produces volatility forecasts for the various stocks under study. In an applied exercise, we consider the panel of asset returns of the constituents of the S&P100 index over the period 2000-2009. Numerical results show that the predictors based on our two-step method outperform existing univariate and multivariate GARCH methods, as well as static factor GARCH models, in the prediction of daily high–low range—while avoiding the usual problems associated with the curse of dimensionality.
Keywords: volatility; dynamic factor models; GARCH models (search for similar items in EconPapers)
JEL-codes: C32 (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (23) Track citations by RSS feed
Published in Journal of Econometrics, 18, August, 2017, 201(2), pp. 307-321. ISSN: 0304-4076
Downloads: (external link)
http://eprints.lse.ac.uk/67455/ Open access version. (application/pdf)
Journal Article: Generalized dynamic factor models and volatilities: estimation and forecasting (2017)
Working Paper: Generalized Dynamic Factor Models and Volatilities: Estimation and Forecasting (2015)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:67455
Access Statistics for this paper
More papers in LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library LSE Library Portugal Street London, WC2A 2HD, U.K.. Contact information at EDIRC.
Bibliographic data for series maintained by LSERO Manager ().