Abstract
In this paper we propose a bootstrap method for quasi-maximum likelihood (QML) estimators in GARCH(1,1) models. The wild bootstrap is used to bootstrap GARCH(1,1) processes and construct bootstrap QML estimators. It is shown that this bootstrap procedure “works” in the sense that it is consistent. Simulation results demonstrate the small sample behaviour of the proposed bootstrap method.
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© 1998 Springer Science+Business Media Dordrecht
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Maercker, G. (1998). Bootstrapping Garch(1,1) Models. In: Refenes, AP.N., Burgess, A.N., Moody, J.E. (eds) Decision Technologies for Computational Finance. Advances in Computational Management Science, vol 2. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-5625-1_16
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DOI: https://doi.org/10.1007/978-1-4615-5625-1_16
Publisher Name: Springer, Boston, MA
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