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

Hosszejni, Darjus ORCID: and Kastner, Gregor ORCID: (2021) Modeling Univariate and Multivariate Stochastic Volatility in R with stochvol and factorstochvol. Journal of Statistical Software, 100 (12). pp. 1-34. ISSN 1548-7660

Available under License Creative Commons: Attribution 4.0 International (CC BY 4.0).

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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.

Item Type: Article
Keywords: Bayesian inference, state-space model, heteroskedasticity, dynamic correlation, dynamic covariance, factor stochastic volatility, Markov chain Monte Carlo, MCMC, leverage effect, asymmetric return distribution, heavy tails, financial time series
Divisions: Departments > Finance, Accounting and Statistics > Statistics and Mathematics
Version of the Document: Published
Depositing User: Gertraud Novotny
Date Deposited: 01 Dec 2021 10:26
Last Modified: 01 Dec 2021 10:26
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