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

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

This note presents the R package bayesGARCH 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 an MCMC sampling algorithm. The usage of the package is shown in an empirical application to exchange rate log-returns.

Original languageEnglish
Pages (from-to)41-47
Number of pages7
JournalThe R Journal
Volume2
Issue number2
Publication statusPublished - 2010

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Generalized Autoregressive Conditional Heteroscedasticity
Bayesian Estimation
Exchange rate
Markov Chain Monte Carlo
Tuning
Innovation
Students
Sampling
Model
Generalized autoregressive conditional heteroscedasticity
Bayesian estimation
Markov chain Monte Carlo
Exchange rates

Cite this

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abstract = "This note presents the R package bayesGARCH 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 an MCMC sampling algorithm. The usage of the package is shown in an empirical application to exchange rate log-returns.",
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Bayesian estimation of the GARCH(1,1) model with student-t innovations. / Ardia, David; Hoogerheide, Lennart F.

In: The R Journal, Vol. 2, No. 2, 2010, p. 41-47.

Research output: Contribution to JournalArticleAcademicpeer-review

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