Modeling Univariate and Multivariate Stochastic Volatility in R with stochvol and factorstochvol

Stochastic volatility (SV) models are nonlinear state-space models that enjoy increasing popularity for fitting and predicting heteroskedastic time series. However, due to the large number of latent quantities, their efficient estimation is non-trivial and software that allows to easily fit SV models to data is rare. We aim to alleviate this issue by presenting novel implementations of five SV models delivered in two R packages. Several unique features are included and documented. As opposed to previous versions, stochvol is now capable of handling linear mean models, conditionally heavy tails, and the leverage effect in combination with SV. Moreover, we newly introduce factorstochvol which caters for multivariate SV. Both packages offer a user-friendly interface through the conventional R generics and a range of tailor-made methods. Computational efficiency is achieved via interfacing R to C++ and doing the heavy work in the latter. In the paper at hand, we provide a detailed discussion on Bayesian SV estimation and showcase the use of the new software through various examples.

Automated summary

Stochastic volatility (SV) models, popular for fitting and predicting heteroskedastic time series, face challenges in efficient estimation due to a large number of latent quantities. The authors address this by introducing novel implementations of five SV models in two R packages, enhancing computational efficiency and offering user-friendly interfaces. Additionally, they discuss Bayesian SV estimation and demonstrate the new software through various examples.