Description Usage Arguments References Examples
An S4 method that takes a simMGarch
object and outputs simulated correlated time series with a piecewise constant covariance matrix.
The correlations are generated as σ_{i, i'} = ρ^{|i-i'|} with ρ taking values from (-1,1). The exact variables that will contain a change-point are
randomly selected and controlled by r
in the simMGarch
object.
1 2 3 4 |
object |
A |
Cho, Haeran, and Karolos Korkas. "High-dimensional GARCH process segmentation with an application to Value-at-Risk." arXiv preprint arXiv:1706.01155 (2017).
1 2 3 4 5 6 7 8 9 10 11 12 | cp=500
n=2000
pw.CCC.obj <- new("simMGarch")
pw.CCC.obj@changepoints=cp
pw.CCC.obj@n=n
pc_Sigma.obj <- pc_Sigma(pw.CCC.obj)
par(mfrow=c(1,2))
#requires corrplot library
#correlation matrix before the changepoint
#corrplot::corrplot.mixed(cor(pc_Sigma.obj@cor_errors[1:cp,]), order="hclust", tl.col="black")
#correlation matrix after the changepoint
#corrplot::corrplot.mixed(cor(pc_Sigma.obj@cor_errors[(cp+1):n,]), order="hclust", tl.col="black")
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