CCCgarch.MM

it first estimates the conditional GARCH variances, then filters out the 
time-varying volatility and estimates the higher order comoments on the innovations 
rescaled such that their unconditional covariance matrix is the conditional covariance matrix forecast

Portfolio optimization and analysis routines and graphics.

Brian G Peterson

PortfolioAnalytics

Portfolio Analysis, Including Numerical Methods for Optimization
of Portfolios

Peter Carl

Ross Bennett

Kris Boudt

Xinran Zhao

R. Douglas Martin

Guy Yollin

Hezky Varon

Xiaokang Feng

Yifu Kang

CCCgarch.MM function

<dl><dt>R</dt>
<dd>an xts, vector, matrix, data frame, timeSeries or zoo object of asset returns</dd>
<dt>momentargs</dt>
<dd>list containing arguments to be passed down to lower level functions, default NULL</dd>
<dt>...</dt>
<dd>any other passthru parameters</dd></dl>

Arguments

compute comoments for use by lower level optimization functions when the conditional covariance matrix is a CCC GARCH model — CCCgarch.MM

<dl>

<dt>R</dt>
<dd>an xts, vector, matrix, data frame, timeSeries or zoo object of asset returns</dd>


<dt>momentargs</dt>
<dd>list containing arguments to be passed down to lower level functions, default NULL</dd>


<dt>...</dt>
<dd>any other passthru parameters</dd>

</dl>

compute comoments for use by lower level optimization functions when the conditional covariance matrix is a CCC GARCH model

CCCgarch.MM: compute comoments for use by lower level optimization functions when the conditional covariance matrix is a CCC GARCH model

Description

Usage

Arguments