Semi-Parametric Modelling of Correlation Dynamics
Christian M. Hafner
Catholic University of Louvain (UCL) - Center for Operations Research and Econometrics (CORE); Tinbergen Institute
Dick J. C. Van Dijk
Erasmus University Rotterdam - Erasmus School of Economics - Econometric Institute; ERIM
Philip Hans Franses
Erasmus University Rotterdam (EUR) - Department of Econometrics
Econometric Institute Report No. EI 2005-26
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.
Number of Pages in PDF File: 47
Keywords: Multivariate GARCH, dynamic conditional correlation, kernel regression, minimum variance portfolio, tracking error minimization
JEL Classification: C14, C32, G11working papers series
Date posted: September 27, 2005
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