Viterbi-based estimation for Markov switching GARCH model

Robert J. Elliott, John W. Lau, Hong Miao, Tak Kuen Siu

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


We 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.

Original languageEnglish
Pages (from-to)219-231
Number of pages13
JournalApplied Mathematical Finance
Issue number3
Publication statusPublished - 2012


Dive into the research topics of 'Viterbi-based estimation for Markov switching GARCH model'. Together they form a unique fingerprint.

Cite this