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

TL;DR

The paper introduces the R package stochvol, which implements Bayesian stochastic volatility modeling using MCMC, enabling inference and prediction of time series volatility with ease and flexibility.

Contribution

It presents a fully Bayesian, MCMC-based R package for stochastic volatility modeling, including mathematical details, sampling schemes, and practical examples.

Findings

Effective volatility prediction from exchange rate data

Flexible integration with other MCMC samplers

User-friendly implementation for Bayesian inference

Abstract

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.