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.
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