Deriving the autocovariances of powers of Markov-switching GARCH models, with applications to statistical inference

Christian Francq and ZakoI¨an, Jean-Michel
Authors registered in the RePEc Author Service: Jean-Michel Zakoian

Computational Statistics & Data Analysis, 2008, vol. 52, issue 6, 3027-3046

Abstract: A procedure is proposed for computing the autocovariances and the ARMA representations of the squares, and higher-order powers, of Markov-switching GARCH models. It is shown that many interesting subclasses of the general model can be discriminated in view of their autocovariance structures. Explicit derivation of the autocovariances allows for parameter estimation in the general model, via a GMM procedure. It can also be used to determine how many ARMA representations are needed to identify the Markov-switching GARCH parameters. A Monte Carlo study and an application to the Standard & Poor index are presented.

Date: 2008
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Citations: View citations in EconPapers (32)

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