MSGARCH-package: The R package MSGARCH

In MSGARCH: Markov-Switching GARCH Models

The R package MSGARCH

Description

The R package MSGARCH implements a comprehensive set of functionalities for Markov-switching GARCH (Haas et al. 2004a) and Mixture of GARCH (Haas et al. 2004b) models, This includes fitting, filtering, forecasting, and simulating. Other functions related to Value-at-Risk and Expected-Shortfall are also available.
The main functions of the package are coded in C++ using Rcpp (Eddelbuettel and Francois, 2011) and RcppArmadillo (Eddelbuettel and Sanderson, 2014).
MSGARCH focuses on the conditional variance (and higher moments) process. Hence, there is no equation for the mean. Therefore, you must pre-filter via AR(1) before applying the model.
The MSGARCH package implements a variety of GARCH specifications together with several conditional distributions. This allows for a rich modeling environment for Markov-switching GARCH models. Each single-regime process is a one-lag process (e.g., GARCH(1,1)). When optimization is performed, we ensure that the variance in each regime is covariance-stationary and strictly positive (refer to the vignette for more information).
We refer to Ardia et al. (2019a) for a detailed introduction to the package and its usage. Refer to Ardia et al. (2018) and Ardia et al. (2019b) for further applications.
The authors acknowledge Google for financial support via the Google Summer of Code 2016 & 2017, the International Institute of Forecasters and Industrielle-Alliance.

Note

By using MSGARCH you agree to the following rules:

• You must cite Ardia et al. (2019a) in working papers and published papers that use MSGARCH. Use citation("MSGARCH").

• You must place the following URL in a footnote to help others find MSGARCH: https://CRAN.R-project.org/package=MSGARCH.

• You assume all risk for the use of MSGARCH.

Author(s)

Maintainer: Keven Bluteau [e-mail] (ORCID)

Authors:

• David Ardia [e-mail] (ORCID)

• Leopoldo Catania [e-mail] (ORCID)

• Denis-Alexandre Trottier [e-mail]

Other contributors:

• Kris Boudt [e-mail] (ORCID) [contributor]

• Alexios Ghalanos [e-mail] [contributor]

• Brian Peterson [e-mail] [contributor]

References

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, 34(4), 733-747. doi: 10.1016/j.ijforecast.2018.05.004

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

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

Eddelbuettel, D. & Francois, R. (2011). Rcpp: Seamless R and C++ integration. Journal of Statistical Software, 40, 1-18. doi: 10.18637/jss.v040.i08

Eddelbuettel, D. & Sanderson, C. (2014). RcppArmadillo: Accelerating R with high-performance C++ linear algebra. Computational Statistics & Data Analysis, 71, 1054-1063. doi: 10.1016/j.csda.2013.02.005

Haas, M. Mittnik, S. & Paolella, MS. (2004). A new approach to Markov-switching GARCH models. Journal of Financial Econometrics, 2, 493-530. doi: 10.1093/jjfinec/nbh020

Haas, M. Mittnik, S. & Paolella, M. S. (2004b). Mixed normal conditional heteroskedasticity. Journal of Financial Econometrics, 2, 211-250. doi: 10.1093/jjfinec/nbh009

See Also

Useful links:

• https://github.com/keblu/MSGARCH

• Report bugs at https://github.com/keblu/MSGARCH/issues

MSGARCH documentation built on Dec. 6, 2022, 1:06 a.m.