segMGarch-package {segMGarch}

Multiple Change-Point Detection for High-Dimensional GARCH Processes

Description

Implements a segmentation algorithm for multiple change-point detection in high-dimensional GARCH processes described in Cho and Korkas (2018) ("High-dimensional GARCH process segmentation with an application to Value-at-Risk." arXiv preprint arXiv:1706.01155). It simultaneously segments GARCH processes by identifying 'common' change-points, each of which can be shared by a subset or all of the component time series as a change-point in their within-series and/or cross-sectional correlation structure. We adopt the Double CUSUM Binary Segmentation procedure Cho (2016), which achieves consistency in estimating both the total number and locations of the multiple change-points while permitting within-series and cross-sectional correlations, for simultaneous segmentation of the panel data of transformed time series.

It also provides additional functions and methods that relate to risk management measures and backtests.

Details

We develop a segmentation algorithm for multiple change-point detection in high-dimensional GARCH processes. It simultaneously segments GARCH processes by identifying 'common' change-points, each of which can be shared by a subset or all of the component time series as a change-point in their within-series and/or cross-sectional correlation structure. The methodology first transforms the d-dimensional time series into d(d+1)/2-dimensional panel data consisting of empirical residual series and their cross-products, whereby change-points in the complex ((un)conditional variance and covariance) structure are made detectable as change-points in the simpler (mean) structure of the panel data at the price of the increased dimensionality. The main routine is garch.seg.

Author(s)

Haeran Cho and Karolos Korkas

Maintainer: Karolos Korkas <[e-mail]>

References

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

Cho, Haeran. "Change-point detection in panel data via double CUSUM statistic." Electronic Journal of Statistics 10, no. 2 (2016): 2000-2038.

Examples

  ## Not run: 
#pw.CCC.obj <- new("simMGarch")
#pw.CCC.obj <- pc_cccsim(pw.CCC.obj)
#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(pw.CCC.obj@y)
#dcs.obj$est.cps
#ts.plot(t(pw.CCC.obj@y),col="grey");grid()
#abline(v=dcs.obj$est.cps,col="red" )
#abline(v=pw.CCC.obj@changepoints,col="blue" )
#legend("bottom", legend=c("Estimated change-points", "Real change-points"), 
#col=c("red", "blue"), lty=1:2, cex=0.8)
  
## End(Not run)