Robust Garch(1,1) Model.

robustGarch

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).

Installation

The package can be installed as following:

devtools::install_github("EchoRLiu/robustGarch")
library(robustGarch)

Example

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)
  
  ret <- PCRA::retOFG
  ret <- ret$RET
  
  (robFitBM <- robGarch(ret, fitMethod = "BM"))
  
  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.

Future Development

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.