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.

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Tinbergen Institute
hdl.handle.net/1765/19377
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 http://hdl.handle.net/1765/19377