This note presents the R package bayesGARCH (Ardia, 2007) which provides functions for the Bayesian estimation of the parsimonious and effective GARCH(1,1) model with Student-t innovations. The estimation procedure is fully automatic and thus avoids the tedious task of tuning a MCMC sampling algorithm. The usage of the package is shown in an empirical application to exchange rate logreturns.

Bayesian, GARCH, Markov Chain Monte Carlo, Student-t, software
Bayesian Analysis (jel C11), Simulation Methods; Monte Carlo Methods; Bootstrap Methods (jel C15), Time-Series Models; Dynamic Quantile Regressions (jel C22)
Tinbergen Institute
Tinbergen Institute Discussion Paper Series
Discussion paper / Tinbergen Institute
Tinbergen Institute

David, D, & Hoogerheide, L.F. (2010). Bayesian Estimation of the GARCH(1,1) Model with Student-t-Innovations (No. TI-045/4). Discussion paper / Tinbergen Institute (pp. 1–7). Tinbergen Institute. Retrieved from