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bayesGARCH

The package bayesGARCH (Ardia and Hoogerheide, 2010) implements in R the Bayesian estimation procedure described in Ardia (2008) for the GARCH(1,1) model with Student-t innovations. The approach consists of a Metropolis-Hastings (MH) algorithm where the proposal distributions are constructed from auxiliary ARMA processes on the squared observations. This methodology avoids the time-consuming and difficult task, especially for non-experts, of choosing and tuning a sampling algorithm.

Please cite the package in publications!

By using bayesGARCH you agree to the following rules:

You must cite Ardia and Hoogerheide (2010) in working papers and published papers that use bayesGARCH.
You must place the following URL in a footnote to help others find bayesGARCH: https://CRAN.R-project.org/package=bayesGARCH.
You assume all risk for the use of bayesGARCH.

Ardia, D., Hoogerheide, L.F. (2010).
Bayesian estimation of the GARCH(1,1) model with Student-t innovations.
R Journal, 2(2), 41-47.
https://doi.org/10.32614/RJ-2010-014

Ardia, D. (2008).
Financial Risk Management with Bayesian Estimation of GARCH Models: Theory and Applications.
volume 612 series Lecture Notes in Economics and Mathematical Systems. Springer-Verlag, Berlin, Germany.
https://doi.org/10.1007/978-3-540-78657-3

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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