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robustGarch is an R package aiming to provide a method for modelling robust Garch processes (RG), addressing the issue of robustness toward additive outliers - instead of innovations outliers. This work is based on Muler and Yohai (2008) (MY).
The package can be installed as following:
::install_github("EchoRLiu/robustGarch")
devtoolslibrary(robustGarch)
This is a basic example which shows you how to fit your daily return time series data into robust Garch(1,1) model.
if (requireNamespace("PCRA", quietly = TRUE)) {
library(robustGarch)
<- PCRA::retOFG
ret <- ret$RET
ret
<- robGarch(ret, fitMethod = "BM"))
(robFitBM
sum(robFitBM$fitted_pars[2:3])
summary(robFitBM)
plot(robFitBM)
else {
} message("PCRA package is not installed. Please install it with install.packages('PCRA') if you want to run this example or use other dataset to replace ret.")
}
For more examples and explanation, please refer to the robustGarch-Vignette.
Any future development will be released in the github page. A few key features will be added to the package in September 2020:
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.