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A Multivariate Generalized Orthogonal Factor GARCH Model

Markku Lanne and Pentti Saikkonen ()

MPRA Paper from University Library of Munich, Germany

Abstract: The paper studies a factor GARCH model and develops test procedures which can be used to test the number of factors needed to model the conditional heteroskedasticity in the considered time series vector. Assuming normally distributed errors the parameters of the model can be straightforwardly estimated by the method of maximum likelihood. Inefficient but computationally simple preliminary estimates are first obtained and used as initial values to maximize the likelihood function. Maximum likelihood estimation with nonnormal errors is also straightforward. Motivated by the empirical application of the paper a mixture of normal distributions is considered. An interesting feature of the implied factor GARCH model is that some parameters of the conditional covariance matrix which are not identifiable in the case of normal errors become identifiable when the mixture distribution is used. As an empirical example we consider a system of four exchange rate return series.

Keywords: Multivariate GARCH model; mixture of normal distributions; exchange rate (search for similar items in EconPapers)
JEL-codes: C32 C51 F31 (search for similar items in EconPapers)
Date: 2005-05
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https://mpra.ub.uni-muenchen.de/23714/1/MPRA_paper_23714.pdf original version (application/pdf)

Related works:
Journal Article: A Multivariate Generalized Orthogonal Factor GARCH Model (2007) Downloads
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