Semiparametric Estimation of Multivariate GARCH Models

Claudio Morana

No 317, Working Papers from University of Milano-Bicocca, Department of Economics

Abstract: 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 ex-post procedure, to ensure well behaved conditional 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.

Keywords: Multivariate GARCH model; dynamic conditional correlation; semiparametric estimation (search for similar items in EconPapers)
JEL-codes: C30 C51 (search for similar items in EconPapers)
Pages: 9
Date: 2015-12-10, Revised 2015-12-10
New Economics Papers: this item is included in nep-ecm and nep-ets
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Citations: View citations in EconPapers (15)

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Related works:
Working Paper: Semiparametric Estimation of Multivariate GARCH Models (2017)
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