In this paper we develop a new semi-parametric model for conditional correlations, which combines parametric univariate GARCH-type specifications for the individual conditional volatilities with nonparametric kernel regression for the conditional correlations. This approach not only avoids the proliferation of parameters as the number of assets becomes large, which typically happens in conventional multivariate conditional volatility models, but also the rigid structure imposed by more parsimonious models, such as the dynamic conditional correlation model. An empirical application to the 30 Dow Jones stocks demonstrates that the model is able to capture interesting asymmetries in correlations and that it is competitive with standard parametric models in terms of constructing minimum variance portfolios and minimum tracking error portfolios.
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Paper provided by Erasmus University Rotterdam, Econometric Institute in its series Econometric Institute Report with number
EI 2005-26 Revision_Date: 2009-11-06.
Cited by: (explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)
Annastiina Silvennoinen & Timo Teräsvirta, 2008.
"Multivariate GARCH models,"
CREATES Research Papers
2008-06, School of Economics and Management, University of Aarhus.
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