Bayesian estimation of the GARCH(1,1) model with Student-t innovations
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.
bayesGARCH in publications:
Ardia, D., Hoogerheide, L.F. (2010).
Bayesian estimation of the GARCH(1,1) model with Student-t innovations.
R Journal 2(2), pp.41-47.
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.