Package: stochvol 3.2.4

Darjus Hosszejni

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.

Authors:Darjus Hosszejni [aut, cre], Gregor Kastner [aut]

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Bug tracker:https://github.com/gregorkastner/stochvol/issues

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48 exports 15 stars 2.78 score 4 dependencies 7 dependents 79 scripts 1.4k downloads

Last updated 7 months agofrom:0bf8fca158. Checks:OK: 7 NOTE: 2. Indexed: yes.

Exports:default_fast_svget_default_fast_svget_default_general_svlatentlatent0logretparaparadensplotparatraceplotpredlatentpredvolapredypriorsruntimesampled_parametersspecify_priorssv_betasv_constantsv_exponentialsv_gammasv_infinitysv_inverse_gammasv_multinormalsv_normalsvbetasvlmsvlsamplesvlsample_rollsvsamplesvsample_fast_cppsvsample_general_cppsvsample_rollsvsample2svsimsvtausvtlsamplesvtlsample_rollsvtsamplesvtsample_rollthinningupdate_fast_svupdate_general_svupdate_regressorsupdate_t_errorupdatesummaryvalidate_and_process_expertvolavolplot

Dependencies:codalatticeRcppRcppArmadillo