Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: David Ardia, Keven Bluteau, Kris Boudt, Leopoldo Catania, Denis-Alexandre Trottier
Title: Markov-Switching GARCH Models in R: The MSGARCH Package
Abstract: We describe the package MSGARCH, which implements Markov-switching GARCH (generalized autoregressive conditional heteroscedasticity) models in R with efficient C++ object-oriented programming. Markov-switching GARCH models have become popular methods to account for regime changes in the conditional variance dynamics of time series. The package MSGARCH allows the user to perform simulations as well as maximum likelihood and Bayesian Markov chain Monte Carlo estimations of a very large class of Markov-switching GARCH-type models. The package also provides methods to make single-step and multi-step ahead forecasts of the complete conditional density of the variable of interest. Risk management tools to estimate conditional volatility, value-at-risk, and expected-shortfall are also available. We illustrate the broad functionality of the MSGARCH package using exchange rate and stock market return data.

Page views:: 8066. Submitted: 2017-10-30. Published: 2019-10-31.
Paper: Markov-Switching GARCH Models in R: The MSGARCH Package     Download PDF (Downloads: 6128)
MSGARCH_2.31.tar.gz: R source package Download (Downloads: 420; 124KB)
v91i04.R: R replication code Download (Downloads: 541; 12KB)
v91i04-timing.R: R replication code (timing comparison) Download (Downloads: 330; 4KB) Replication materials Download (Downloads: 344; 14KB)

DOI: 10.18637/jss.v091.i04

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.