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

Autor: Darjus Hosszejni, Gregor Kastner
Jazyk: angličtina
Rok vydání: 2021
Předmět:
FOS: Computer and information sciences
Multivariate statistics
101018 Statistik
J.4
state-space model
502025 Ökonometrie
Computer science
Bayesian inference
leverage effect
Econometrics (econ.EM)
financial time series
Quantitative Finance - Computational Finance
Markov chain Monte Carlo (MCMC)
Computation (stat.CO)
102022 Softwareentwicklung
Stochastic volatility
dynamic correlation
G.3
101026 Zeitreihenanalyse
symbols
Statistics
Probability and Uncertainty
HA29-32
Algorithm
heteroskedasticity
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
Statistics and Probability
Heteroscedasticity
Bayesian probability
Computational Finance (q-fin.CP)
Statistics - Computation
factor stochastic volatility
asymmetric return distribution
HB135-147
FOS: Economics and business
symbols.namesake
QA76.75-76.765
62-04 (Primary) 62F15
62M10
62P20 (Secondary)
Economics - Econometrics
Statistical Finance (q-fin.ST)
101018 Statistics
Univariate
Quantitative Finance - Statistical Finance
Markov chain Monte Carlo
102022 Software development
101026 Time series analysis
heavy tails
Range (mathematics)
502025 Econometrics
dynamic covariance
Software
Zdroj: Journal of Statistical Software; Vol. 100 (2021); 1-34
ISSN: 1548-7660
Popis: 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.
Databáze: OpenAIRE
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