mtCopula {MTS} | R Documentation |
Multivariate t-Copula Volatility Model
Description
Fits a t-copula to a k-dimensional standardized return series. The correlation matrices are parameterized by angles and the angles evolve over time via a DCC-type equation.
Usage
mtCopula(rt, g1, g2, grp = NULL, th0 = NULL, m = 0,
include.th0 = TRUE, ub=c(0.95,0.049999))
Arguments
rt |
A T-by-k data matrix of k standardized time series (after univariate volatility modeling) |
g1 |
lamda1 parameter, nonnegative and less than 1 |
g2 |
lambda2 parameter, nonnegative and satisfying lambda1+lambda2 < 1. |
grp |
a vector to indicate the number of assets divided into groups. Default means each individual asset forms a group. |
th0 |
initial estimate of theta0 |
m |
number of lags used to estimate the local theta-angles |
include.th0 |
A logical switch to include theta0 in estimation. Default is to include. |
ub |
Upper bound of parameters |
Value
estimates |
Parameter estimates |
Hessian |
Hessian matrix |
rho.t |
Cross-correlation matrices |
theta.t |
Time-varying angel matrices |
Author(s)
Ruey S. Tsay
References
Tsay (2014, Chapter 7). Multivariate Time Series Analysis with R and Financial Applications. John Wiley. Hoboken, NJ.