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

Ardia, David and Hoogerheide, Lennart F. (2009): Bayesian estimation of the GARCH(1,1) model with Student-t innovations. Published in: The R Journal , Vol. 2, No. 2 (31 December 2010): pp. 41-47.

Abstract

This paper presents the R package bayesGARCH which provides functions for the Bayesian estimation of the parsimonious but effective GARCH(1,1) model with Student-t innovations. The estimation procedure is fully automatic and thus avoids the time-consuming and difficult task of tuning a sampling algorithm. The usage of the package is shown in an empirical application to exchange rate log-returns.

• Item Type: MPRA Paper
• Language: English
• Keywords: GARCH; Bayesian; MCMC; Student-t; R software
• Subjects: C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C52 - Model Evaluation, Validation, and Selection; C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes; C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C15 - Statistical Simulation Methods: General; C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C11 - Bayesian Analysis: General

References

Ardia D (2007). `bayesGARCH': Bayesian Estimation of the GARCH(1,1) Model with Student-t Innovations in R. URL http://CRAN.R-project.org/package=AdMit

Ardia D (2008). Financial Risk Management with Bayesian Estimation of GARCH Models: Theory and Applications, volume 612 of Lecture Notes in Economics and Mathematical Systems. Springer-Verlag, Berlin, Germany. ISBN 978-3-540-78656-6. doi:10.1007/978-3-540-78657-3

Deschamps PJ (2006). A Flexible Prior Distribution for Markov Switching Autoregressions with Student-t Errors. Journal of Econometrics, 133(1), 153-190. doi:10.1016/j.jeconom.2005.03.012

Engle RF (1982). Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica, 50(4), 987-1008.

Plummer M, Best N, Cowles K, Vines K (2008). coda: Output Analysis and Diagnostics for MCMC in R. R package version 0.13-3, URL http://CRAN.R-project.org/package=coda

R Development Core Team (2008). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org