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A Bayesian MCMC Algorithm for Markov Switching GARCH models

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  • Dhiman Das
  • B.Hark Yoo

Abstract

Markov switching GARCH models have been developed in order to address the statistical regularity observed in financial time series such as strong persistence of conditional variance. However, Maximum Likelihood Estimation faces a implementation problem since the conditional variance depends on all the past history of state. This paper shows that this problem can be handled easily in Bayesian inference. A new Markov Chain Monte Carlo algorithm is introduced and proves to work well in a numerical example.

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Bibliographic Info

Paper provided by Econometric Society in its series Econometric Society 2004 Far Eastern Meetings with number 451.

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Date of creation: 11 Aug 2004
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Handle: RePEc:ecm:feam04:451

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Keywords: Bayesian Analysis; GARCH; Markov Switching.;

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Cited by:
  1. Luc Bauwens & Arie Preminger & Jeroen V.K. Rombouts, 2007. "Theory and Inference for a Markov-Switching GARCH Model," Cahiers de recherche 0733, CIRPEE.

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