factorstochvol: Bayesian Estimation of (Sparse) Latent Factor Stochastic Volatility Models

Gregor Kastner

factorstochvol: Bayesian Estimation of (Sparse) Latent Factor Stochastic Volatility Models

Gregor Kastner

Statistics and Mathematics

Publikation: Elektronische/multimediale Veröffentlichungen › Software

Abstract

Markov chain Monte Carlo (MCMC) sampler for fully Bayesian estimation of latent factor stochastic volatility models. Sparsity can be achieved through the usage of Normal-Gamma priors on the factor loading matrix.

Originalsprache	Englisch
Publikationsstatus	Veröffentlicht - 2017

Österreichische Systematik der Wissenschaftszweige (ÖFOS)

102022 Softwareentwicklung
101018 Statistik
502025 Ökonometrie
101026 Zeitreihenanalyse

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Zitat

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title = "factorstochvol: Bayesian Estimation of (Sparse) Latent Factor Stochastic Volatility Models",

abstract = "Markov chain Monte Carlo (MCMC) sampler for fully Bayesian estimation of latent factor stochastic volatility models. Sparsity can be achieved through the usage of Normal-Gamma priors on the factor loading matrix.",

author = "Gregor Kastner",

year = "2017",

language = "English",

}

TY - ADVS

T1 - factorstochvol: Bayesian Estimation of (Sparse) Latent Factor Stochastic Volatility Models

AU - Kastner, Gregor

PY - 2017

Y1 - 2017

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AB - Markov chain Monte Carlo (MCMC) sampler for fully Bayesian estimation of latent factor stochastic volatility models. Sparsity can be achieved through the usage of Normal-Gamma priors on the factor loading matrix.

M3 - Software

ER -