plotVol: Plotting volatilities of time series

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

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 (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 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, 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)

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