Tails, Extremes and ACF of the GOGARCH process
What is it about?
GO-GARCH processes are often used to model heavy-tailed (financial) data. Some issues arise, though, if our data presents different marginal tail indexes which may imply a good central fit but a poor fit to the extremes.
Why is it important?
The possibility of modeling heavy tails using GARCH models has been rigorously established in the univariate case, and the consequences that this heavy tailedness has on the distributional limits of the sample autocovariance function are well known. In the multivariate case, however, the results are not fully developed or understood. We show that the sample autocovariance function for the GOGARCH process may either have a random limit or converge rather slowly to its population counterpart depending on how heavy the tails of the process are in accordance to the CCC-GARCH and the univariate GARCH cases.
The following have contributed to this page: Dr Nelson O Muriel
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