Efficient Bayesian Inference for Stochastic Volatility (SV) Models
Default Values for the Expert Settings
Common Extractors for 'svdraws' and 'svpredict' Objects
Computes the Log Returns of a Time Series
Probability Density Function Plot for the Parameter Posteriors
Trace Plot of MCMC Draws from the Parameter Posteriors
Trace Plot of MCMC Draws from the Parameter Posteriors
Graphical Summary of the Posterior Distribution
Graphical Summary of the Posterior Predictive Distribution
Prediction of Future Returns and Log-Volatilities
Specify Prior Distributions for SV Models
Efficient Bayesian Inference for Stochastic Volatility (SV) Models
Prior Distributions in stochvol
Markov Chain Monte Carlo (MCMC) Sampling for the Stochastic Volatility...
Markov Chain Monte Carlo (MCMC) Sampling for the Stochastic Volatility...
Bindings to C++ Functions in stochvol
Rolling Estimation of Stochastic Volatility Models
Simulating a Stochastic Volatility Process
Single MCMC Update Using Fast SV
Single MCMC Update Using General SV
Single MCMC update of Bayesian linear regression
Single MCMC update to Student's t-distribution
Updating the Summary of MCMC Draws
Validate and Process Argument 'expert'
Plotting Quantiles of the Latent Volatilities
Efficient algorithms for fully Bayesian estimation of stochastic volatility (SV) models with and without asymmetry (leverage) via Markov chain Monte Carlo (MCMC) methods. Methodological details are given in Kastner and Frühwirth-Schnatter (2014) <doi:10.1016/j.csda.2013.01.002> and Hosszejni and Kastner (2019) <doi:10.1007/978-3-030-30611-3_8>; the most common use cases are described in Hosszejni and Kastner (2021) <doi:10.18637/jss.v100.i12> and Kastner (2016) <doi:10.18637/jss.v069.i05> and the package examples.
Useful links