文章基本信息

标题：Markov-Switching GARCH Models in R: The MSGARCH Package
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作者：David Ardia ; Keven Bluteau ; Kris Boudt 等
期刊名称：Journal of Statistical Software
印刷版ISSN：1548-7660
电子版ISSN：1548-7660
出版年度：2019
卷号：91
期号：1
页码：1-38
DOI：10.18637/jss.v091.i04
出版社：University of California, Los Angeles
摘要：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.
关键词：GARCH; MSGARCH; Markov-switching; conditional volatility; forecasting; R software.
其他关键词：GARCH;MSGARCH;Markov-switching;conditional volatility;forecasting;R software