plotVol {bayesDccGarch} | R Documentation |
Plotting volatilities of time series
Description
Plotting method for volatilities of time series.
Usage
plotVol(mY, vol, ts.names=paste("TS_", 1:ncol(mY), sep=""), colors = c("grey","red"), ...)
Arguments
mY |
a matrix of the data ( |
vol |
a matrix ( |
ts.names |
a vector of length |
colors |
a vector with name of the colors for plotting the returns and volatilities. |
... |
additional arguments for |
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
, plot.bayesDccGarch
Examples
data(DaxCacNik)
mY = DaxCacNik
out = bayesDccGarch(mY)
## The code
plotVol(mY, out$H[,c("H_1,1","H_2,2","H_3,3")], c("DAX","CAC40","NIKKEI"))
## gives the result of ##
plot(out)