Viterbi-Based Estimation for Markov Switching GARCH Model
AbstractWe outline a two-stage estimation method for a Markov-switching Generalized Autoregressive Conditional Heteroscedastic (GARCH) model modulated by a hidden Markov chain. The first stage involves the estimation of a hidden Markov chain using the Vitberi algorithm given the model parameters. The second stage uses the maximum likelihood method to estimate the model parameters given the estimated hidden Markov chain. Applications to financial risk management are discussed through simulated data.
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Bibliographic InfoArticle provided by Taylor & Francis Journals in its journal Applied Mathematical Finance.
Volume (Year): 19 (2012)
Issue (Month): 3 (August)
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Web page: http://www.tandfonline.com/RAMF20
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