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:
devtools::install_github("EchoRLiu/robustGarch")
library(robustGarch)This is a basic example which shows you how to fit your daily return time series data into robust Garch(1,1) model.
data(gspc)
fit <- robGarch(gspc, methods = "BM", fixed_pars = c(0.8, 3.0), optimizer="Rsolnp", stdErr_method = "numDeriv")
summary(fit)
plot(fit)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:
- Fix the issue with singularity error with Hessian matrix
- Statistics tests such as std_error, t_value, p_value for Garch parameters
- Code debug on model filter for M model and QML
- More optimization choices
- Extension to robust Garch(p, q)
- Name changes for better collaboration
