GO-GARCH: a multivariate generalized orthogonal GARCH model

Multivariate GARCH specifications are typically determined by means of practical considerations such as the ease of estimation, which often results in a serious loss of generality. A new type of multivariate GARCH model is proposed, in which potentially large covariance matrices can be parameterized...

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Bibliographic Details
Published in:Journal of applied econometrics (Chichester, England) England), 2002-09, Vol.17 (5), p.549-564
Main Author: van der Weide, Roy
Format: Article
Language:English
Subjects:
Algorithms
Correlations
Covariance matrices
Degrees of freedom
Econometrics
Economic models
Eigenvalues
Estimating techniques
Estimation
Estimators
Linear transformations
Mathematical methods
Maximum likelihood estimation
Parametric models
Random variables
Rotation
Statistical variance
Stochastic models
Studies
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Summary:Multivariate GARCH specifications are typically determined by means of practical considerations such as the ease of estimation, which often results in a serious loss of generality. A new type of multivariate GARCH model is proposed, in which potentially large covariance matrices can be parameterized with a fairly large degree of freedom while estimation of the parameters remains feasible. The model can be seen as a natural generalization of the O-GARCH model, while it is nested in the more general BEKK model. In order to avoid convergence difficulties of estimation algorithms, we propose to exploit unconditional information first, so that the number of parameters that need to be estimated by means of conditional information is more than halved. Both artificial and empirical examples are included to illustrate the model.
ISSN:0883-7252
1099-1255
DOI:10.1002/jae.688