plotVol {bayesDccGarch} | R Documentation |
Plotting method for volatilities of time series.
plotVol(mY, vol, ts.names=paste("TS_", 1:ncol(mY), sep=""), colors = c("grey","red"), ...)
mY |
a matrix of the data (n \times k). |
vol |
a matrix (n \times k) with the volatility estimates. |
ts.names |
a vector of length k with the names of the time series. |
colors |
a vector with name of the colors for plotting the returns and volatilities. |
... |
additional arguments for |
Ricardo Sandes Ehlers, Jose Augusto Fiorucci and Francisco Louzada
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. http://dx.doi.org/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. http://adsabs.harvard.edu/abs/2014arXiv1412.2967F.
bayesDccGarch-package
, bayesDccGarch
, plot.bayesDccGarch
data(DaxCacNik) mY = DaxCacNik[1:10,] # more data is necessary out = bayesDccGarch(mY, nSim=1000) ## 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)