predict.bayesDccGarch {bayesDccGarch} | R Documentation |
Bayesian forecast for volatilities and coditional correlations
Description
Bayesian forecast for volatilities and coditional correlations
Usage
## S3 method for class 'bayesDccGarch'
predict(object, ..., n_ahead = 5, bayes = T)
Arguments
object |
a bayesDccGarch object |
... |
default argument of predict function, not used |
n_ahead |
number of steps ahead forecast |
bayes |
a boolean. If True, then the forecast is calculated as being the average of the forecasts across all states in the Markov chain (much slower). If False then predictions are calculated using estimation parameters (much faster). |
Value
A list with elements H
and R
References
Engle, R.F. and Sheppard, K. Theoretical and empirical properties of dynamic conditional correlation multivariate GARCH, 2001, NBER Working Paper.
Examples
out = bayesDccGarch(DaxCacNik)
predict.bayesDccGarch(out, n_ahead=5)
[Package bayesDccGarch version 3.0.4 Index]