Dealing with Stochastic Volatility in Time Series Using the R Package stochvol

Dealing with Stochastic Volatility in Time Series Using the R Package stochvol

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 variable...

Full description

Bibliographic Details
Main Author: Kastner, Gregor
Format: Others
Language:en
Published: Foundation for Open Access Statistics 2016
Online Access:http://epub.wu.ac.at/4890/1/v69i05.pdf
id ndltd-VIENNA-oai-epub.wu-wien.ac.at-4890
record_format oai_dc
spelling ndltd-VIENNA-oai-epub.wu-wien.ac.at-48902018-06-05T06:07:07Z Dealing with Stochastic Volatility in Time Series Using the R Package stochvol Kastner, Gregor 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. (author's abstract) Foundation for Open Access Statistics 2016 Article PeerReviewed en application/pdf http://epub.wu.ac.at/4890/1/v69i05.pdf Creative Commons: Attribution 3.0 Austria http://dx.doi.org/10.18637/jss.v069.i05 http://www.jstatsoft.org/ http://www.foastat.org/ 10.18637/jss.v069.i05 http://epub.wu.ac.at/4890/
collection NDLTD
language en
format Others
sources NDLTD
description 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. (author's abstract)
author Kastner, Gregor
spellingShingle Kastner, Gregor
Dealing with Stochastic Volatility in Time Series Using the R Package stochvol
author_facet Kastner, Gregor
author_sort Kastner, Gregor
title Dealing with Stochastic Volatility in Time Series Using the R Package stochvol
title_short Dealing with Stochastic Volatility in Time Series Using the R Package stochvol
title_full Dealing with Stochastic Volatility in Time Series Using the R Package stochvol
title_fullStr Dealing with Stochastic Volatility in Time Series Using the R Package stochvol
title_full_unstemmed Dealing with Stochastic Volatility in Time Series Using the R Package stochvol
title_sort dealing with stochastic volatility in time series using the r package stochvol
publisher Foundation for Open Access Statistics
publishDate 2016
url http://epub.wu.ac.at/4890/1/v69i05.pdf
work_keys_str_mv AT kastnergregor dealingwithstochasticvolatilityintimeseriesusingtherpackagestochvol
_version_ 1718691277510279168

Similar Items