variance_covariance: Variance-covariance matrix
In LFUrmutils: LFU Risk Management Utilities

Description Usage Arguments Details Value Note Author(s) References See Also Examples

Function to extract the variance-covariance matrix of multivariate volatility models. Currently only implemented for models of class MultiEWMA.

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varcov(object, ...)
## S3 method for class 'MultiEWMA'
varcov(object, offdiagonal = TRUE, duplicates = TRUE, ...)

`object`	Output of a multivariate volatility model.
`offdiagonal`	Whether diagonal elements should be kept or not. `TRUE` (the default) or `FALSE`.
`duplicates`	Whether duplicate off-diagonal elements should be kept or not. `TRUE` (the default) or `FALSE`.
`...`	Arguments passed to methods.

Returns the variance-covariance matrix of a multivariate volatility model.

A multivariate zoo object. Each row of the output objects represents the correlation matrix (or elements thereof) of the corresponding day.

If the full conditional variance-covariance matrices are needed, users can also use fitted, which is slightly faster. However, in comparison to fitted, varcov gives the user more control over the extracted elements of the conditional variance-covariance matrices. Moreover, in future releases of this package, varcov may be defined for other multivariate volatility models as well. In this case, the function will provide a convenient infrastructure to better compare the output of these models.

Bernhard Eder

Danielsson (2011). Financial Risk Forecasting. Wiley. Chichester.
Jorion (2007). Value at Risk, 3rd. McGraw-Hill. New York.
Ruppert and Matteson (2015). Statistics and Data Analysis for Financial Engineering, 2nd. Springer. New York.

MultiEWMA, ccor, fitted

# Load returns
data("CRSPday_zoo")
y <- CRSPday_zoo

# Compute multivariate EWMA model
ewma <- MultiEWMA(y, center = TRUE)

# Extract variance covariance matrix
EWMAvc <- varcov(ewma)
plot(EWMAvc)