MTS: mtCopula – R documentation

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mtCopula

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

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

v1.0

Artistic License 2.0

Authors

Ruey S. Tsay and David Wood

Initial release

2018-10-8