# bayesGARCH

The package `bayesGARCH` ([Ardia and Hoogerheide, 2010)](https://doi.org/10.32614/RJ-2010-014) implements in R 
the Bayesian estimation procedure described 
in [Ardia (2008)](https://doi.org/10.1007/978-3-540-78657-3) 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 the package in publications!

By using `bayesGARCH` you agree to the following rules: 

1) You must cite [Ardia and Hoogerheide (2010)](https://doi.org/10.32614/RJ-2010-014) in working papers and published papers that use `bayesGARCH`.
2) You must place the following URL in a footnote to help others find `bayesGARCH`: [https://CRAN.R-project.org/package=bayesGARCH](https://CRAN.R-project.org/package=bayesGARCH). 
3) 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](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](https://doi.org/10.1007/978-3-540-78657-3)    
