dccPre: Preliminary Fitting of DCC Models

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

Preliminary Fitting of DCC Models

Description

This program fits marginal GARCH models to each component of a vector return series and returns the standardized return series for further analysis. The garchFit command of fGarch package is used.

Usage

dccPre(rtn, include.mean = T, p = 0, cond.dist = "norm")

Arguments

rtn	A T-by-k data matrix of k-dimensional asset returns
include.mean	A logical switch to include a mean vector. Deafult is to include the mean.
p	VAR order for the mean equation
cond.dist	The conditional distribution of the innovations. Default is Gaussian.

Details

The program uses fGarch package to estimate univariate GARCH model for each residual series after a VAR(p) fitting, if any.

Value

marVol	A matrix of the volatility series for each return series
sresi	Standardized residual series
est	Parameter estimates for each marginal volatility model
se.est	Standard errors for parameter estimates of marginal volatility models

Note

fGarch package is used

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

dccFit

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