mtCopula: Multivariate t-Copula Volatility Model

In MTS: All-Purpose Toolkit for Analyzing Multivariate Time Series (MTS) and Estimating Multivariate Volatility Models

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.

MTS documentation built on April 11, 2022, 5:07 p.m.