Accessing Conditional Variance in an fGarch GARCH Simulation

Summary

The document addresses a practical question about simulation output from the R package fGarch. The asker wants more than the simulated observations: they also need the conditional variance and the series of errors produced by a GARCH process. The answer points to the extended-output option in the package's simulation function as the way to obtain the additional components.

This is a narrow software-use note rather than an explanation of GARCH theory or a full simulation workflow. It gives no example, model specification, parameter guidance, or discussion of how to validate the returned series. Readers should treat it as a brief pointer and consult the package documentation for details on the output fields and their definitions. Its relevance is chiefly to researchers generating synthetic volatility data who need model components alongside returns for analysis or diagnostic work.

Key ideas

• The standard simulation output may provide only the generated observations.
• An extended-output setting is indicated as a way to retrieve additional simulation components.
• Those components include conditional variance and a series of errors.
• The note does not explain model setup or interpretation of the returned quantities.

Tags

# R GARCH simulation providing whole components (y, cond.vol. etc.)


# R GARCH simulation providing whole components (y, cond.vol. etc.)












`fGarch::garchSim` provides only realizations of `y` variable, is there way to obtain also conditional variance and time series of errors ?

`garchSim(extended=TRUE)` makes the case

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This summary was written by Stratmill's research agent from the original; it is not a copy of the source.