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stochvol (version 2.0.4)

Efficient Bayesian Inference for Stochastic Volatility (SV) Models

Description

Efficient algorithms for fully Bayesian estimation of stochastic volatility (SV) models via Markov chain Monte Carlo (MCMC) methods. Methodological details are given in Kastner and Frhwirth-Schnatter (2014) ; the most common use cases are described in Kastner (2016) . Also incorporates SV with leverage.

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Version

Install

install.packages('stochvol')

Monthly Downloads

1,769

Version

2.0.4

License

GPL (>= 2)

Maintainer

Darjus Hosszejni

Last Published

June 26th, 2019

Functions in stochvol (2.0.4)

paratraceplot.svdraws

Trace Plot of MCMC Draws from the Parameter Posteriors
logret

Computes the Log Returns of a Time Series
paradensplot

Probability Density Function Plot for the Parameter Posteriors
plot.svpredict

Graphical Summary of the Posterior Predictive Distribution
arpredict

Dynamic prediction for the AR-SV model (deprecated)
paratraceplot

Trace Plot of MCMC Draws from the Parameter Posteriors
predict.svdraws

Prediction of Future Returns and Log-Volatilities
extractors

Common Extractors for 'svdraws' Objects
plot.svdraws

Graphical Summary of the Posterior Distribution
exrates

Euro exchange rate data
svsample2

Minimal overhead version of svsample.
svsim

Simulating a Stochastic Volatility Process
svlsample

Markov Chain Monte Carlo (MCMC) Sampling for the Stochastic Volatility Model with Leverage (SVL)
stochvol-package

Efficient Bayesian Inference for Stochastic Volatility (SV) Models
updatesummary

Updating the Summary of MCMC Draws
volplot

Plotting Quantiles of the Latent Volatilities
svsample

Markov Chain Monte Carlo (MCMC) Sampling for the Stochastic Volatility (SV) Model
svlsample2

Minimal overhead version of svlsample.