Model and distribution uncertainty in multivariate GARCH estimation: a Monte Carlo analysis

Eduardo Rossi and Filippo Spazzini ()

MPRA Paper from University Library of Munich, Germany

Abstract: Multivariate GARCH models are in principle able to accommodate the features of the dynamic conditional correlations processes, although with the drawback, when the number of financial returns series considered increases, that the parameterizations entail too many parameters.In general, the interaction between model parametrization of the second conditional moment and the conditional density of asset returns adopted in the estimation determines the fitting of such models to the observed dynamics of the data. This paper aims to evaluate the interactions between conditional second moment specifications and probability distributions adopted in the likelihood computation, in forecasting volatilities and covolatilities. We measure the relative performances of alternative conditional second moment and probability distributions specifications by means of Monte Carlo simulations, using both statistical and financial forecasting loss functions.

Keywords: Multivariate GARCH models; Model uncertainty; Quasi-maximum likelihood; Monte Carlo methods (search for similar items in EconPapers)
JEL-codes: C01 C32 C52 (search for similar items in EconPapers)
Date: 2008
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for and nep-ore
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Related works:
Journal Article: Model and distribution uncertainty in multivariate GARCH estimation: A Monte Carlo analysis (2010)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:12260

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