Type: Package
Title: Robust Garch(1,1) Model
Version: 0.4.2
Description: A method for modeling robust generalized autoregressive conditional heteroskedasticity (Garch) (1,1) processes, providing robustness toward additive outliers instead of innovation outliers. This work is based on the methodology described by Muler and Yohai (2008) <doi:10.1016/j.jspi.2007.11.003>.
License: MIT + file LICENSE
Encoding: UTF-8
URL: https://github.com/EchoRLiu/robustGarch
BugReports: https://github.com/EchoRLiu/robustGarch/issues
RoxygenNote: 7.3.2
Suggests: rmarkdown, testthat, PCRA
Imports: Rsolnp, nloptr, rugarch, zoo, xts
Depends: R (≥ 4.3.0)
NeedsCompilation: no
Packaged: 2025-04-27 18:12:42 UTC; yuhongliu
Author: Echo Liu [aut, cre], Daniel Xia [aut], R. Douglas Martin [aut]
Maintainer: Echo Liu <yuhong.echo.liu@gmail.com>
Repository: CRAN
Date/Publication: 2025-04-28 18:50:02 UTC

Robust GARCH Package

Description

Robust GARCH modeling functions.

Author(s)

Maintainer: Echo Liu yuhong.echo.liu@gmail.com

Authors:

See Also

Useful links:

Robust GARCH(1,1) Model Estimation

Description

Computes "BM" robust Garch(1,1) model parameter estimate by using a bounded objective function and a bounded conditional variance recursion. Alternatively, it computes: (1) "M" estimates by using only the bounded objective function, (2) "QML" estimates based on a typically incorrect assumption of normally distributed innovations, (3) "t-MLE" estimates based on an assumption of an innovations t-distributed MLE with unknown location, scale,and degrees of freedom parameters. CHECK IF (3) IS CORRECT.

Usage

robGarch(
  data,
  fitMethod = c("BM", "M", "QML", "MLE"),
  robTunePars = c(0.8, 3),
  optChoice = c("Rsolnp", "nloptr", "nlminb"),
  initialPars = c(5e-04, 0.15, 0.75),
  SEmethod = c("numDeriv", "optim", "sandwich"),
  optControl = list(trace = 0)
)

Arguments

data

an xts object

fitMethod

character valued name of fitting method, one of "BM", "M" "QML" or "tMLE", with "BM" the default value.

robTunePars

a numeric vector c(cM,cFlt) that controls the extent of fitMethod robustness, with default c(0.8,3.0).

optChoice

character valued optChoice name, one of "Rsolnp", "nloptr", "nlminb", with default "Rsolnp".

initialPars

numeric user-defined initial parameters c(gamma0, alpha0, beta0) for use by optChoice, with default values c(0.0005, 0.15, 0.75).

SEmethod

character valued name of standard error method, one of "numDeriv", "optim", "sandwich", with default "numDeriv".

optControl

list of arguments passed to optChoice, with default list(trace=0).

Details

The "BM" fit method delivers the highest robustness by using a half-Huber psi function to bound the normal distribution log-likelihood, and using a Huber psi function to prevent the propagation of influential outliers in the variance recursion. The "M" method is obtained by dropping the BM bounding of the variance recursion, and is therefore less robust toward outliers.

ECHO OR DAN, PLEASE PROVIDE DETAILS FOR optControl. For details of the list of control arguments, please refer to nloptr::nloptr, Rsolnp::solnp, nlminb. The SEmethod default "numDeriv" is based on the Hessian from the optimization.

Value

A list object of class “robustGarch” with components:

data

the input xts object

fitMethod

the the fitMethod specified

robtunePars

the robtunePars specified

initialPars

the initialPars specified

optChoice

the optChoice specified

coefEstimates

computed parameter estimates

sigma

conditional standard deviation xts class time series

SEmethod

the specidied of calculating standard errors

observedInfoMat

observed information matrix

optDetails

a list containing the optChoice specified, the control values specified, and the optChoice minimized objective, and convergence status message

References

Muler, N. and Yohai, V. (2008). Robust estimates for GARCH models. Journal of Statistical Planning and Inference, 138, 2918-2940.

Examples

if (requireNamespace("PCRA", quietly = TRUE)) {
  ret <- PCRA::retOFG
  ret <- ret$RET
  (robFitBM <- robGarch(ret, fitMethod = "BM"))
  sum(robFitBM$fitted_pars[2:3])
  summary(robFitBM)
}

Summary for robustGARCH class

Description

Summarizes the results of a robust GARCH(1,1) model fit by extracting key model components.

Usage

## S3 method for class 'robustGARCH'
summary(object, digits = 3, ...)

## S3 method for class 'robustGARCH'
print(x, digits = 3, ...)

## S3 method for class 'robustGARCH'
plot(
  x,
  digits = 3,
  estimation_pos = "topleft",
  line_name_pos = "topright",
  par_ = par(no.readonly = TRUE),
  pctReturn_ = TRUE,
  abs_ = TRUE,
  original_ = FALSE,
  main_name = "Conditional Volatility (vs |pctReturns(%)|)",
  ...
)

## S3 method for class 'robustGARCH'
coef(object, ...)

aef(fit, nu = 5)

Arguments

object

Same as fit, for summary.robustGARCH

digits

the number of digits for print and plot, default is 3.

...

# to be written

x

Same as fit, for plot.robustGARCH and print.robustGARCH

estimation_pos

string that determines the legend position that specifies gamma, alpha, beta estimations. Choice of "bottomright", "bottom", "bottomleft", "left", "topleft", "top", "topright", "right" and "center". Default is "topleft".

line_name_pos

string that determines the legend position that specifies the names of lines in the plot. Choice of "bottomright", "bottom", "bottomleft", "left", "topleft", "top", "topright", "right" and "center". Default is "topright".

par_

graphical parameters that can be set, which is in the form of par(...). The default is par(no.readonly = TRUE).

pctReturn_

a logical argument. IF TRUE, the plot function will plot the returns in percentage instead of original. Default is TRUE.

abs_

a logical argument, when TRUE, the plot function will plot abs(returns) with conditional standard deviation instead of returns, default to TRUE.

original_

a logical argument. If TRUE, the original return will be plotted. Default is FALSE

main_name

the title of the plot, default is "Conditional SD (vs returns)"

fit

A robustGARCH fit object of class robGarch

nu

degrees of freedom in a Student's t-distribution.

Value

A list of class "summary.robustGARCH" containing:

method

The fitting method used (e.g., "BM", "M", "QML", or "MLE").

coefficients

Named vector of parameter estimates.

loglikelihood

The value of the objective function at convergence.

converged

Logical; indicates whether the optimizer converged successfully.

Examples

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)
  print(robFitBM)
  plot(robFitBM)
  coef(robFitBM)
} else {
  message("Run install.packages('PCRA') to run this example.")
}