Function reference • stochvol

Preface The front page of the manual
`stochvol-package` `stochvol`	Efficient Bayesian Inference for Stochastic Volatility (SV) Models
Simulation Sampling routines, simulation, and prediction
`svlm()`	Markov Chain Monte Carlo (MCMC) Sampling for the Stochastic Volatility (SV) Model
`svsample()` `svtsample()` `svlsample()` `svtlsample()` `svsample2()`	Markov Chain Monte Carlo (MCMC) Sampling for the Stochastic Volatility (SV) Model
`svsample_roll()` `svtsample_roll()` `svlsample_roll()` `svtlsample_roll()`	Rolling Estimation of Stochastic Volatility Models
`svsample_fast_cpp()` `svsample_general_cpp()`	Bindings to `C++` Functions in `stochvol`
`svsim()`	Simulating a Stochastic Volatility Process
`predict(<svdraws>)`	Prediction of Future Returns and Log-Volatilities
Reporting Plotting, extraction, and summary
`plot(<svdraws>)`	Graphical Summary of the Posterior Distribution
`plot(<svpredict>)`	Graphical Summary of the Posterior Predictive Distribution
`paradensplot()`	Probability Density Function Plot for the Parameter Posteriors
`paratraceplot()`	Trace Plot of MCMC Draws from the Parameter Posteriors
`paratraceplot(<svdraws>)`	Trace Plot of MCMC Draws from the Parameter Posteriors
`volplot()`	Plotting Quantiles of the Latent Volatilities
`updatesummary()`	Updating the Summary of MCMC Draws
`para()` `latent0()` `latent()` `vola()` `svbeta()` `svtau()` `priors()` `thinning()` `runtime()` `sampled_parameters()` `predy()` `predlatent()` `predvola()`	Common Extractors for 'svdraws' and 'svpredict' Objects
Prior Distributions Specification of prior distributions for the sample routines
`specify_priors()`	Specify Prior Distributions for SV Models
`sv_constant()` `sv_normal()` `sv_multinormal()` `sv_gamma()` `sv_inverse_gamma()` `sv_beta()` `sv_exponential()` `sv_infinity()`	Prior Distributions in `stochvol`
C++ Documentation for the C++ functions
`update_fast_sv()`	Single MCMC Update Using Fast SV
`update_general_sv()`	Single MCMC Update Using General SV
`update_regressors()`	Single MCMC update of Bayesian linear regression
`update_t_error()`	Single MCMC update to Student's t-distribution
Expert Settings Helper functions for the ‘expert’ argument of the sampling routines
`get_default_fast_sv()` `get_default_general_sv()` `default_fast_sv`	Default Values for the Expert Settings
`validate_and_process_expert()`	Validate and Process Argument 'expert'
Other Remaining manual pages
`exrates`	Euro exchange rate data
`logret()`	Computes the Log Returns of a Time Series

Reference

Preface

Simulation

Reporting

Prior Distributions

C++

Expert Settings

Other