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A specification class to create an object of a simulated piecewise constant conditional correlation (CCC) model denoted by r_t = (r_{1, t}, …, r_{n, t})^T, t=1, …, n with r_{i, t}= √{h_{i, t}}ε_{i, t} where h_{i, t}= ω_i(t) + ∑_{j=1}^p α_{i, j}(t)r_{i, t-j}^2 + ∑_{k=1}^q β_{i, k}(t)h_{i, t-k}. In this package, we assume a piecewise constant CCC with p=q=1.
y
The n \times d time series.
cor_errors
The n \times d matrix of the errors.
h
The n \times d matrix of the time-varying variances.
n
Size of the time series.
d
The number of variables (assets).
r
A sparsity parameter to conrol the impact of changepoint across the series.
multp
A parameter to control the covariance of errors.
changepoints
The vector with the location of the changepoints.
pw
A logical parameter to allow for changepoints in the error covariance matrix.
a0
The vector of the parameters a0 in the individual GARCH processes denoted by ω_i(t) in the above formula.
a1
The vector of the parameters a1 in the individual GARCH processes denoted by α_i(t) in the above formula.
b1
The vector of the parameters b1 in the individual GARCH processes denoted by β_i(t) in the above formula.
BurnIn
The size of the burn-in sample. Note that this only applies at the first simulated segment. Default is 50.
Cho, Haeran, and Karolos Korkas. "High-dimensional GARCH process segmentation with an application to Value-at-Risk." arXiv preprint arXiv:1706.01155 (2017).
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