Package: stochvolTMB 0.3.0

stochvolTMB: Likelihood Estimation of Stochastic Volatility Models

Parameter estimation for stochastic volatility models using maximum likelihood. The latent log-volatility is integrated out of the likelihood using the Laplace approximation. The models are fitted via 'TMB' (Template Model Builder) (Kristensen, Nielsen, Berg, Skaug, and Bell (2016) <doi:10.18637/jss.v070.i05>).

Authors:Jens Wahl [aut, cre]

stochvolTMB_0.3.0.tar.gz
stochvolTMB_0.3.0.zip(r-4.7-x86_64)stochvolTMB_0.3.0.zip(r-4.6-x86_64)stochvolTMB_0.3.0.zip(r-4.5-x86_64)
stochvolTMB_0.3.0.tgz(r-4.6-x86_64)stochvolTMB_0.3.0.tgz(r-4.6-arm64)stochvolTMB_0.3.0.tgz(r-4.5-x86_64)stochvolTMB_0.3.0.tgz(r-4.5-arm64)
stochvolTMB_0.3.0.tar.gz(r-4.7-arm64)stochvolTMB_0.3.0.tar.gz(r-4.7-x86_64)stochvolTMB_0.3.0.tar.gz(r-4.6-arm64)stochvolTMB_0.3.0.tar.gz(r-4.6-x86_64)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
stochvolTMB/json (API)

# Install 'stochvolTMB' in R:

install.packages('stochvolTMB', repos = c('https://jenswahl.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/jenswahl/stochvoltmb/issues

Uses libs:

c++– GNU Standard C++ Library v3

Datasets:

spy - Daily closing prices for the S&P500 from 2005 to 2018.

On CRAN:

cpp

4.96 score 8 stars 23 scripts 390 downloads 8 exports 31 dependencies

Last updated from:578af55cca. Checks:11 ERROR, 1 OK, 1 FAIL. Indexed: yes.

Target	Result	Time
linux-devel-arm64	ERROR	194
linux-devel-x86_64	ERROR	168
source / vignettes	OK	1521
linux-release-arm64	ERROR	175
linux-release-x86_64	ERROR	165
macos-release-arm64	ERROR	129
macos-release-x86_64	ERROR	251
macos-oldrel-arm64	ERROR	131
macos-oldrel-x86_64	ERROR	417
windows-devel	ERROR	191
windows-release	ERROR	141
windows-oldrel	ERROR	160
wasm-release	FAIL	114

Exports:demo estimate_parameters get_nll logit plot_forecast residuals sim_sv simulate_parameters

Dependencies:cli cpp11 data.table farver ggplot2 glue gtable isoband labeling lattice lifecycle MASS Matrix MatrixModels mnormt numDeriv quantreg R6 RColorBrewer Rcpp RcppEigen rlang S7 scales sn SparseM survival TMB vctrs viridisLite withr

stochvolTMB: Likelihood estimation of stochastic volatility

Rendered fromstochvolTMB_vignette.Rmdusingknitr::rmarkdown

Last update: 2026-01-27
Started: 2020-07-31

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Run shiny demo	demo
Estimate parameters for the stochastic volatility model	estimate_parameters
Logit transformation from the real line to (-1, 1).	logit
Plot the estimated latent volatility process	plot.stochvolTMB
Predict future returns and future volatilities	predict.stochvolTMB
Calculate one-step-ahead (OSA) residuals for stochastic volatility model.	residuals
Simulate log-returns from a stochastic volatility model	sim_sv
Simulate from the asymptotic distribution of the parameter estimates	simulate_parameters
Daily closing prices for the S&P500 from 2005 to 2018.	spy
Summary tables of model parameters	summary.stochvolTMB
Calculate quantiles based on predictions from the predictive distribution	summary.stochvolTMB_predict