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

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

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

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

Darjus Hosszejni

https://www.jstatsoft.org/article/view/v100i12

WU Vienna University
of Economics and Busines

November 2021

Fajar bagus W

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English

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