Markov-Switching GARCH Models

The MSGARCH package offers methods to fit (by Maximum Likelihood or MCMC/Bayesian), simulate, and forecast various Markov-Switching GARCH models as described in Ardia et al. (2017) <>.


Changes in Version 0.17 o Fixed many bugs when the number of states were above 2. o Changed MLE estimation scheme o Function ht now output the conditional variance and not the conditional volatility o do.init is now FALSE by default o do.enhance.theta0 is now TRUE by default Known bugs in version 0.17 o Sometimes do.enhance.theta0 gives an error concerning uniroot Changes in Version 0.16 o First release

Reference manual

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1.1 by Keven Bluteau, 8 days ago

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Authors: David Ardia [aut], Keven Bluteau [aut, cre], Kris Boudt [ctb], Leopoldo Catania [aut], Brian Peterson [ctb], Denis-Alexandre Trottier [aut]

Documentation:   PDF Manual  

Task views: Empirical Finance

GPL (>= 2) license

Imports Rcpp, adaptMCMC, coda, methods, zoo, expm, fanplot, MASS, numDeriv

Suggests mcmc, testthat

Linking to Rcpp, RcppArmadillo

See at CRAN