predict.bayesDccGarch

predict

Bayesian forecast for volatilities and coditional correlations

Bayesian estimation of dynamic conditional correlation GARCH model for multivariate time series volatility (Fioruci, J.A., Ehlers, R.S. and Andrade-Filho, M.G., (2014). <doi:10.1080/02664763.2013.839635>.

Jose Augusto Fiorucci

bayesDccGarch

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

Ricardo Sanders Ehlers

Francisco Louzada

predict.bayesDccGarch function

<dl><dt>object</dt>
<dd>a bayesDccGarch object</dd>
<dt>...</dt>
<dd>default argument of predict function, not used</dd>
<dt>n_ahead</dt>
<dd>number of steps ahead forecast</dd>
<dt>bayes</dt>
<dd>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).</dd></dl>

Arguments

Bayesian forecast for volatilities and coditional correlations — predict.bayesDccGarch

<dl>

<dt>object</dt>
<dd>a bayesDccGarch object</dd>


<dt>...</dt>
<dd>default argument of predict function, not used</dd>


<dt>n_ahead</dt>
<dd>number of steps ahead forecast</dd>


<dt>bayes</dt>
<dd>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).</dd>

</dl>

predict.bayesDccGarch: Bayesian forecast for volatilities and coditional correlations

Description

Usage

Value

Arguments

References

Examples