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Editors-in-chief: Bettina Grün, Torsten Hothorn, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Dealing with Stochastic Volatility in Time Series Using the R Package stochvol | Kastner | Journal of Statistical Software
Authors: Gregor Kastner
Title: Dealing with Stochastic Volatility in Time Series Using the R Package stochvol
Abstract: 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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Paper: Dealing with Stochastic Volatility in Time Series Using the R Package stochvol     Download PDF (Downloads: 1493)
Supplements:
stochvol_1.2.3.tar.gz: R source package Download (Downloads: 205; 3MB)
v69i05.R: R replication code Download (Downloads: 315; 41KB)

DOI: 10.18637/jss.v069.i05

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Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.