Conditional Heteroskedasticity Driven by Hidden Markov Chains

Christian Francq, Michel Roussignol and Jean-Michel Zakoian

Journal of Time Series Analysis, 2001, vol. 22, issue 2, 197-220

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.

Date: 2001
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Citations: View citations in EconPapers (38)

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https://doi.org/10.1111/1467-9892.00219

Related works:
Working Paper: Conditional Heteroskedasticity Driven by Hidden Markov Chains (1998)
Working Paper: Conditional heteroskedasticity driven by hidden Markov chains (1998)
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Persistent link: https://EconPapers.repec.org/RePEc:bla:jtsera:v:22:y:2001:i:2:p:197-220

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