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|Title:||A suggestion for constructing a large time-varying conditional covariance matrix|
|Authors:||Gibson, Heather D.|
Hall, Stephen G.
Tavlas, George S.
|Citation:||Economics Letters, 2017, 156, pp. 110-113|
|Abstract:||The construction of large conditional covariance matrices has posed a problem in the empirical literature because the direct extension of the univariate GARCH model to a multivariate setting produces large numbers of parameters to be estimated as the number of equations rises. A number of procedures have previously aimed to simplify the model and restrict the number of parameters, but these procedures typically involve either invalid or undesirable restrictions. This paper suggests an alternative way forward, based on the GARCH approach, which allows conditional covariance matrices of unlimited size to be constructed. The procedure is computationally straightforward to implement. At no point in the procedure is it necessary to estimate anything other than a univariate GARCH model.|
|Embargo on file until:||28-Apr-2019|
|Rights:||Copyright © Elsevier, 2017. After an embargo period this version of the paper will be an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.|
|Description:||The file associated with this record is under embargo until 24 months after publication, in accordance with the publisher's self-archiving policy. The full text may be available through the publisher links provided above.|
|Appears in Collections:||Published Articles, Dept. of Economics|
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|A suggestion for estimating large multivariate conditional covariance matricies april 2017.pdf||Post-review (final submitted author manuscript)||752.19 kB||Adobe PDF||View/Open|
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