stochvol

Darjus Hosszejni; Gregor Kastner

stochvol

Darjus Hosszejni^*
, Gregor Kastner

^*Korrespondierende*r Autor*in für diese Arbeit

Statistics and Mathematics

Publikation: Elektronische/multimediale Veröffentlichungen › Software

Abstract

Efficient algorithms for fully Bayesian estimation of stochastic volatility (SV) models with and without asymmetry (leverage) via Markov chain Monte Carlo (MCMC) methods. Methodological details are given in Kastner and Frühwirth-Schnatter (2014) <doi:10.1016/j.csda.2013.01.002> and Hosszejni and Kastner (2019) <doi:10.1007/978-3-030-30611-3_8>; the most common use cases are described in Kastner (2016) <doi:10.18637/jss.v069.i05> and the package vignette.

Originalsprache	Englisch
Erscheinungsort	Comprehensive R Archive Network (CRAN)
Herausgeber (Verlag)	The R Foundation
Medium	Online
Publikationsstatus	Veröffentlicht - 12 Juli 2021

Österreichische Systematik der Wissenschaftszweige (ÖFOS)

102022 Softwareentwicklung
101018 Statistik
502025 Ökonometrie
101026 Zeitreihenanalyse

Zugriff auf Dokument

https://cran.r-project.org/package=stochvolLizenz: GNU GPL

1 Konferenzposter
1 Originalbeitrag in Fachzeitschrift
1 Populärwissenschaftlicher Artikel

Modeling Univariate and Multivariate Stochastic Volatility in R with stochvol and factorstochvol
Hosszejni, D. & Kastner, G., 2021, in: Journal of Statistical Software. 100, 12, S. 1 - 34
Publikation: Wissenschaftliche Fachzeitschrift › Originalbeitrag in Fachzeitschrift › Begutachtung

Open Access
Datei

142 Downloads (Pure)
stochvol 2.0.0 - Stochastic volatility models with leverage in R
Hosszejni, D. & Kastner, G., 26 März 2019, ISBA Bulletin, 26, 1, S. 8-15.
Publikation: Populärwissenschaftliche Artikel (z.B. Magazine) › Populärwissenschaftlicher Artikel

Open Access
Efficient Estimation of the Stochastic Volatility Model with Leverage
Hosszejni, D. & Kastner, G., 2018.
Publikation: Konferenzbeitrag › Konferenzposter

Open Access
Datei

Zitat

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title = "stochvol",

abstract = "Efficient algorithms for fully Bayesian estimation of stochastic volatility (SV) models with and without asymmetry (leverage) via Markov chain Monte Carlo (MCMC) methods. Methodological details are given in Kastner and Fr{\"u}hwirth-Schnatter (2014) and Hosszejni and Kastner (2019) ; the most common use cases are described in Kastner (2016) and the package vignette.",

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TY - ADVS

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AU - Hosszejni, Darjus

AU - Kastner, Gregor

PY - 2021/7/12

Y1 - 2021/7/12

N2 - Efficient algorithms for fully Bayesian estimation of stochastic volatility (SV) models with and without asymmetry (leverage) via Markov chain Monte Carlo (MCMC) methods. Methodological details are given in Kastner and Frühwirth-Schnatter (2014) and Hosszejni and Kastner (2019) ; the most common use cases are described in Kastner (2016) and the package vignette.

AB - Efficient algorithms for fully Bayesian estimation of stochastic volatility (SV) models with and without asymmetry (leverage) via Markov chain Monte Carlo (MCMC) methods. Methodological details are given in Kastner and Frühwirth-Schnatter (2014) and Hosszejni and Kastner (2019) ; the most common use cases are described in Kastner (2016) and the package vignette.

M3 - Software

PB - The R Foundation

CY - Comprehensive R Archive Network (CRAN)

ER -

stochvol

Abstract

Österreichische Systematik der Wissenschaftszweige (ÖFOS)

Zugriff auf Dokument

Publikationen

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

stochvol 2.0.0 - Stochastic volatility models with leverage in R

Efficient Estimation of the Stochastic Volatility Model with Leverage

Zitat