bayesGARCH

Bayesian estimation of the GARCH(1,1) model with Student-t innovations

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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 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.
https://journal.r-project.org/archive/2010-2/

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.
http://dx.doi.org/10.1007/978-3-540-78657-3