dccFit: Dynamic Cross-Correlation Model Fitting

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

Dynamic Cross-Correlation Model Fitting

Description

Fits a DCC model using either multivariate Gaussian or multivariate Student-t innovations. Two types of DCC models are available. The first type is proposed by Engle and the other is by Tse and Tsui. Both models appear in the Journal of Business and Economic Statistics, 2002.

Usage

dccFit(rt, type = "TseTsui", theta = c(0.90, 0.02), 
    ub = c(0.95, 0.049999), lb = c(0.4,0.00001),
    cond.dist = "std", df = 7, m = 0)

Arguments

rt	The T-by-k data matrix of k-dimensional standardized asset returns. Typically, they are the standardized residuals of the command dccPre.
type	A logical switch to specify the type of DCC model. Type="TseTsui" for Tse and Tsui's DCC model. Type = "Engle" for Engle's DCC model. Default is Tse-Tsui model.
theta	The initial parameter values for theta1 and theta2
ub	Upper bound of parameters
lb	Lower bound of parameters
cond.dist	Conditional innovation distribution with std for multivariate Student-t innovations.
df	degrees of freedom of the multivariate Student-t innovations.
m	For Tse and Tsui method only, m denotes the number of returns used in local correlation matrix estimation

Value

estimates	Parameter estimates
Hessian	Hessian matrix of the estimates
rho.t	Time-varying correlation matrices. Each row contains elements of a cross-correlation matrix.

Author(s)

Ruey S. Tsay

References

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

See Also

dccPre

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