segMGarch (version 1.3)

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

Description

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.

Usage

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 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 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.

References

Cho, H. and Korkas, K.K., 2022. High-dimensional GARCH process segmentation with an application to Value-at-Risk. Econometrics and Statistics, 23, pp.187-203.

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)