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GitHub - EchoRLiu/robustGarch: robGarch package - GSoC 2020 Project

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

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