garch.seg-class function

An S4 method to detect the change-points in a high-dimensional GARCH process.

An S4 method to detect the change-points in a high-dimensional GARCH process using the DCBS methodology described in Cho and Korkas (2018). If a tvMGarch is specified then it returns a tvMGarch object is returned. Otherwise a list of features is returned. methods

garch.seg(object, x, p = 1, q = 0, f = NULL, sig.level = 0.05,
  Bsim = 200, off.diag = TRUE, dw = NULL, do.pp = TRUE,
  do.parallel = 4)

## S4 method for signature 'ANY'
garch.seg(object = NULL, x, p = 1, q = 0, f = NULL,
  sig.level = 0.05, Bsim = 200, off.diag = TRUE, dw = NULL,
  do.pp = TRUE, do.parallel = 4)

## S4 method for signature 'tvMGarch'
garch.seg(object, p = 1, q = 0, f = NULL,
  sig.level = 0.05, Bsim = 200, off.diag = TRUE, dw = NULL,
  do.pp = TRUE, do.parallel = 4)

Arguments

• object: A tvMGarch object. Not necessary if x is used.
• x: Input data matrix, with each row representing the component time series.
• p: Choose the ARCH order. Default is 1.
• q: Choose the GARCH order. Default is 0.
• f: The dampening factor. If NULL then f is selected automatically. Default is NULL.
• sig.level: Indicates the quantile of bootstrap test statistics to be used for threshold selection. Default is 0.05.
• Bsim: Number of bootstrap samples for threshold selection. Default is 200.
• off.diag: If TRUE allows to look at the cross-sectional correlation structure.
• dw: The length of boundaries to be trimmed off.
• do.pp: Allows further post processing of the estimated change-points to reduce the risk of undersegmentation.
• do.parallel: Number of copies of R running in parallel, if do.parallel = 0, %do% operator is used, see also foreach .

Examples

#pw.CCC.obj <- new("simMGarch")
#pw.CCC.obj@d=10
#pw.CCC.obj@n=1000
#pw.CCC.obj@changepoints=c(250,750)
#pw.CCC.obj <- pc_cccsim(pw.CCC.obj)
#dcs.obj=garch.seg(x=empirObj@y,do.parallel = 4)

References

Cho, Haeran, and Karolos Korkas. "High-dimensional GARCH process segmentation with an application to Value-at-Risk." arXiv preprint arXiv:1706.01155 (2018).