stochvol

This is the development repository of the R package stochvol. You find the same information as a pkgdown website here.

Features

The package provides methods to estimate the stochastic volatility model, potentially with conditionally heavy tails and/or with leverage. Using functions svsample, svtsample, svlsample, and svtlsample, one can conduct Bayesian inference on all parameters, including the time-varying volatilities (the states in the state space). The same functionality is reachable using the formula interface of svlm.

Additional features:

• Prediction, plotting, residual extraction work with the usual functions in R (predict, plot, and residuals)
• Choose from a range of prior distrubutions; see help("specify_priors", package="stochvol")
• Built-in support for linear regression and autoregressive processes with stochastic volatility errors; look for function argument designmatrix
• Easy interfacing with bayesplot functions through the as.array() specialization
• Rolling or expanding window estimation can be used for backtesting; see help("svsample_roll", package="stochvol")
• Run independent Markov chains using R's cross-platform parallelization; look for function arguments n_chains, parallel, n_cpus, and cl (for "cluster")
• For plug&play Bayesian modeling, when stochastic volatility is part of a larger model, fast-access functions can speed up execution in R; see help("svsample_fast_cpp", package="stochvol")
• For advanced users, there is a C++ interface; see e.g. help("update_fast_sv", package="stochvol")
• For teaching purposes, you can fix any parameter to a known value using sv_constant as the prior specification

Install CRAN Version

Type into your R session:

install.packages("stochvol")

For more information, please visit the CRAN page of the package.

Install Latest Development Version

Type into your R session:

if (!require("remotes")) {
  install.packages("remotes")
}
remotes::install_github(
  repo = "https://github.com/gregorkastner/stochvol")

Documentation

The best introduction is the combination of our vignettes:

• Dealing with Stochastic Volatility in Time Series Using the R Package stochvol
• Modeling Univariate and Multivariate Stochastic Volatility in R with stochvol and factorstochvol

For individual functions, please refer to the help pages after installing the package. For instance, for svsample, execute

help("svsample", package = "stochvol")

For more information, please visit stochvol's CRAN page.