Viterbi-based estimation for Markov switching GARCH model

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

doi:10.1080/1350486X.2011.620396

Viterbi-based estimation for Markov switching GARCH model

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

Department of Applied Finance

Research output: Contribution to journal › Article › peer-review

6 Citations (Scopus)

Abstract

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 language	English
Pages (from-to)	219-231
Number of pages	13
Journal	Applied Mathematical Finance
Volume	19
Issue number	3
DOIs	https://doi.org/10.1080/1350486X.2011.620396
Publication status	Published - 2012

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10.1080/1350486X.2011.620396

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AB - 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.

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Viterbi-based estimation for Markov switching GARCH model

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