Markov-switching GARCH models in R

Introduction

Markov-switching GARCH models have become popular to account for regime changes in the conditional variance dynamics of financial time series. The R package MSGARCH (Ardia et al., 2019) implements Markov-switching GARCH-type models very efficiently by using C++ object-oriented programming techniques. It allows the user to perform simulations as well as Maximum Likelihood and MCMC/Bayesian 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.

Installation

The latest stable version of MSGARCH is available on CRAN and can be installed via:

  R > install.packages("MSGARCH")

To install the latest development version of MSGARCH (which may contain bugs!) use these lines:

  R > install.packages("devtools")
  R > require("devtools")
  R > devtools::install_github("keblu/MSGARCH", subdir="Package")

References

Please cite MSGARCH in publications:

Ardia, D., Bluteau, K., Boudt, K., Catania, L., Trottier, D.-A. (2019).
Markov-switching GARCH models in R: The MSGARCH package.
Journal of Statistical Software, Vol 91, Issue 4, pp. 1-38.
http://doi.org/10.18637/jss.v091.i04

Ardia, D., Bluteau, K., Boudt, K., Catania, L. (2018).
Forecasting risk with Markov-switching GARCH models: A large-scale performance study.
International Journal of Forecasting, Vol 34, Issue 4, pp. 733-747.
https://doi.org/10.1016/j.ijforecast.2018.05.004

Ardia, D., Bluteau, K., Ruede, M. (2019).
Regime changes in Bitcoin GARCH volatility dynamics.
Finance Research Letters, Vol 29, pp. 266-271.
https://doi.org/10.1016/j.frl.2018.08.009

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