Semi-Parametric Modelling of Correlation Dynamics

Abstract

In this paper we develop a new semi-parametric model for conditional correlations, which combines parametric univariate Generalized Auto Regressive Conditional Heteroskedasticity 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.

Citation

Hafner, C.M., van Dijk, D. and Hans Franses, P. (2006), "Semi-Parametric Modelling of Correlation Dynamics", Terrell, D. and Fomby, T.B. (Ed.) Econometric Analysis of Financial and Economic Time Series (Advances in Econometrics, Vol. 20 Part 1), Emerald Group Publishing Limited, Leeds, pp. 59-103. https://doi.org/10.1016/S0731-9053(05)20003-8