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Conditional Heteroskedasticity Driven by Hidden Markov Chains

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  • Christian Francq

    (Crest)

  • Michel Roussignol

    (Crest)

  • Jean-Michel Zakoïan

    (Crest)

Abstract

We consider a generalized autoregressive conditionally heteroskedastic (GARCH) equation where the coefficients depend on the state of a nonobserved Markov chain. Necessary and sufficient conditions ensuring the existence of a stationary solution are given. In the case of ARCH regimes, the maximum likelihood estimates are shown to be consistent. The identification problem is also considered. This is illustrated by means of real and simulated data sets.
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Suggested Citation

  • Christian Francq & Michel Roussignol & Jean-Michel Zakoïan, 1998. "Conditional Heteroskedasticity Driven by Hidden Markov Chains," Working Papers 98-45, Center for Research in Economics and Statistics.
  • Handle: RePEc:crs:wpaper:98-45
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