Quotation Kastner, Gregor. 2016. Dealing with Stochastic Volatility in Time Series Using the R Package stochvol. Journal of Statistical Software. 69 (5), 1-30.




The R package stochvol provides a fully Bayesian implementation of heteroskedasticity modeling within the framework of stochastic volatility. It utilizes Markov chain Monte Carlo (MCMC) samplers to conduct inference by obtaining draws from the posterior distribution of parameters and latent variables which can then be used for predicting future volatilities. The package can straightforwardly be employed as a stand-alone tool; moreover, it allows for easy incorporation into other MCMC samplers. The main focus of this paper is to show the functionality of stochvol. In addition, it provides a brief mathematical description of the model, an overview of the sampling schemes used, and several illustrative examples using exchange rate data.


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Publication's profile

Status of publication Published
Affiliation WU
Type of publication Journal article
Journal Journal of Statistical Software
Citation Index SCI
WU-Journal-Rating new FIN-A
Language English
Title Dealing with Stochastic Volatility in Time Series Using the R Package stochvol
Volume 69
Number 5
Year 2016
Page from 1
Page to 30
Reviewed? Y
URL https://www.jstatsoft.org/article/view/v069i05
DOI https://doi.org/10.18637/jss.v069.i05
Open Access Y
Open Access Link https://www.jstatsoft.org/article/view/v069i05


Kastner, Gregor (Details)
Institute for Statistics and Mathematics IN (Details)
Research areas (Ă–STAT Classification 'Statistik Austria')
1105 Computer software (Details)
1162 Statistics (Details)
5323 Econometrics (Details)
5701 Applied statistics (Details)
5707 Time series analysis (Details)
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