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

Gregor Kastner

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

Gregor Kastner

Institute for Statistics and Mathematics

Publication: Non-textual form › 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.

Original language	English
Publication status	Published - 2017

Austrian Classification of Fields of Science and Technology (ÖFOS)

102022 Software development
101018 Statistics
502025 Econometrics
101026 Time series analysis

Cite this

@misc{9aba1721e818451a80263b5056ae6142,

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

N2 - 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.

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 -