plot.bayesDccGarch: Plotting volatilities for Bayesian DCC-GARCH model

In bayesDccGarch: Methods and Tools for Bayesian Dynamic Conditional Correlation GARCH(1,1) Model

Plotting volatilities for Bayesian DCC-GARCH model

Description

Produces a plot of time series and the volatilities. This is a particular case of plotVol function.

Usage

## S3 method for class 'bayesDccGarch'
plot(x, ts.names=NULL, colors = c("grey","red"), ...)

Arguments

x	Object of class “bayesDccGarch”.
ts.names	a vector of length k with the names of the time series.
colors	a vector with the colors for plotting the returns and volatilities.
...	additional arguments for plot function

Value

No return value

Author(s)

Ricardo Sandes Ehlers, Jose Augusto Fiorucci and Francisco Louzada

References

Fioruci, J.A., Ehlers, R.S., Andrade Filho, M.G. Bayesian multivariate GARCH models with dynamic correlations and asymmetric error distributions, Journal of Applied Statistics, 41(2), 320–331, 2014a. <doi:10.1080/02664763.2013.839635>

Fioruci, J.A., Ehlers, R.S., Louzada, F. BayesDccGarch - An Implementation of Multivariate GARCH DCC Models, ArXiv e-prints, 2014b. https://ui.adsabs.harvard.edu/abs/2014arXiv1412.2967F/abstract.

See Also

bayesDccGarch-package, bayesDccGarch, plotVol

Examples

data(DaxCacNik)

mY = DaxCacNik

out = bayesDccGarch(mY, nSim=1000)
plot(out)

bayesDccGarch documentation built on April 22, 2023, 9:08 a.m.