Mulivariate 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

1
mtCopula(rt, g1, g2, grp = NULL, th0 = NULL, m = 0, include.th0 = TRUE)

Arguments

rt

A T-by-k data matrix of k standardized time series (after univariate volatility modeling)

g1

lamda1 parameter, nononegative 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 inlcude.

Value

estimates

Parameter estimates

Hessian

Hessian matrix

rho.t

Cross-correlation matrices

theta.t

Time-varying angel mtrices

Author(s)

Ruey S. Tsay

References

Tsay (2014, Chapter 7). Multivariate Time Series Analysis with R and Financial Applications. John Wiley. Hoboken, NJ.

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