Elliott, Robert J., authorLau, John W., authorMiao, Hong, authorKuen Siu, Tak, authorApplied Mathematical Finance, publisher2020-05-182020-05-182011-08-22Elliott, R., Lau, J., Miao, H., & Kuen Siu, T. (2012). Viterbi-Based Estimation for Markov Switching GARCH Model. Applied Mathematical Finance, 19(3), 219–231. https://doi.org/10.1080/1350486X.2011.620396https://hdl.handle.net/10217/206882Includes bibliographical references (pages 18-21).Published as: Applied Mathematical Finance, vol.19, no. 3, pp.219-231, July 2012, https://doi.org/10.1080/1350486X.2011.620396.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.born digitalarticleseng©2013 Informa UK Limited. Author can archive pre-print and post-print.Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.volatilityregime switchingGARCHViterbi algorithmreference probabilityfiltermaximum likelihood estimationvalue at riskText