Template-Type: ReDIF-Paper 1.0
Author-Name: Hafner, C.M.
Author-Name-Last: Hafner
Author-Name-First: Christian
Author-Person: pha77
Author-Name: van Dijk, D.J.C.
Author-Name-Last: van Dijk
Author-Name-First: Dick
Author-Person: pva27
Author-Name: Franses, Ph.H.B.F.
Author-Name-Last: Franses
Author-Name-First: Philip Hans
Author-Person: pfr226
Title: Semi-Parametric Modelling of Correlation Dynamics
Abstract: 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.
Creation-Date: 2005-07-01
File-URL: https://repub.eur.nl/pub/6849/ei200526.pdf
File-Format: application/pdf
Series: RePEc:ems:eureir
Number: EI 2005-26
Classification-JEL: C14, C32, G11
Keywords: dynamic conditional correlation, kernel regression, minimum variance portfolio, multivariate GARCH, tracking error minimization
Handle: RePEc:ems:eureir:6849