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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| Veröffentlicht in: | Journal of applied econometrics (Chichester, England) England), 2002-09, Vol.17 (5), p.549-564 |
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| container_title | Journal of applied econometrics (Chichester, England) |
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| creator | van der Weide, Roy |
| description | 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. |
| doi_str_mv | 10.1002/jae.688 |
| format | Article |
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| source | Jstor Complete Legacy; Wiley Online Library Journals Frontfile Complete |
| 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 |
| title | GO-GARCH: a multivariate generalized orthogonal GARCH model |
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