stochvol - Efficient Bayesian Inference for Stochastic Volatility (SV) Models
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 Hosszejni and Kastner (2021) <doi:10.18637/jss.v100.i12> and Kastner (2016) <doi:10.18637/jss.v069.i05> and the package examples.
Last updated 5 months ago
openblascpp
8.16 score 15 stars 8 dependents 90 scripts 2.2k downloadsfactorstochvol - Bayesian Estimation of (Sparse) Latent Factor Stochastic Volatility Models
Markov chain Monte Carlo (MCMC) sampler for fully Bayesian estimation of latent factor stochastic volatility models with interweaving <doi:10.1080/10618600.2017.1322091>. Sparsity can be achieved through the usage of Normal-Gamma priors on the factor loading matrix <doi:10.1016/j.jeconom.2018.11.007>.
Last updated 1 years ago
openblascpp
4.73 score 7 stars 1 dependents 17 scripts 641 downloads