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The paper introduces a new simple semiparametric estimator of the conditional variance-covariance and correlation matrix SP-DCC. While sharing a similar sequential approach to existing dynamic conditional correlation DCC methods, SP-DCC has the advantage of not requiring the direct parameterization of the conditional covariance or correlation processes, therefore also avoiding any assumption on their long-run target. In the proposed framework, conditional variances are estimated by univariate GARCH models, for actual and suitably transformed series, in the first step; the latter are then nonlinearly combined in the second step, according to basic properties of the covariance and correlation operator, to yield nonparametric estimates of the various conditional covariances and correlations. Moreover, in contrast to available DCC methods, SP-DCC allows for straightforward estimation also for the non-symultaneous case, i.e. for the estimation of conditional cross-covariances and correlations, displaced at any time horizon of interest. A simple expost procedure to ensure well behaved conditional variance-covariance and correlation matrices, grounded on nonlinear shrinkage, is finally proposed. Due to its sequential implementation and scant computational burden, SP-DCC is very simple to apply and suitable for the modeling of vast sets of conditionally heteroskedastic time series.


Multivariate GARCH Model, Dynamic Conditional Correlation, Semiparametric Estimation

Cite this paper

Morana, C. 2015 Semiparametric Estimation of Multivariate GARCH Models. Open Journal of Statistics, 5, 852-858. doi: 10.4236-ojs.2015.57083.

Autor: Claudio Morana1,2,3



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