Semi-Parametric Modelling of Correlation Dynamics
Christian Hafner (),
Dick van Dijk () and
Philip Hans Franses
No EI 2005-26, Econometric Institute Research Papers from Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute
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.
Keywords: dynamic conditional correlation; kernel regression; minimum variance portfolio; multivariate GARCH; tracking error minimization (search for similar items in EconPapers)
JEL-codes: C14 C32 G11 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:ems:eureir:6849
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