Journal Article

GO-GARCH: A Multivariate Generalized Orthogonal GARCH Model

Roy van der Weide
Journal of Applied Econometrics
Vol. 17, No. 5, Special Issue: Modelling and Forecasting Financial Volatility (Sep. - Oct., 2002), pp. 549-564
Published by: Wiley
Stable URL: https://www.jstor.org/stable/4129271
Page Count: 16
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GO-GARCH: A Multivariate Generalized Orthogonal GARCH Model
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Abstract

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.

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