Viterbi-based estimation for markov switching GARCH model
Date
2011-08-22
Authors
Elliott, Robert J., author
Lau, John W., author
Miao, Hong, author
Kuen Siu, Tak, author
Applied Mathematical Finance, publisher
Journal Title
Journal ISSN
Volume Title
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.
Description
Includes 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.
Published as: Applied Mathematical Finance, vol.19, no. 3, pp.219-231, July 2012, https://doi.org/10.1080/1350486X.2011.620396.
Rights Access
Subject
volatility
regime switching
GARCH
Viterbi algorithm
reference probability
filter
maximum likelihood estimation
value at risk