Package: stochvolTMB 0.2.0

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 Christian Wahl <[email protected]>

stochvolTMB_0.2.0.tar.gz
stochvolTMB_0.2.0.zip(r-4.5)stochvolTMB_0.2.0.zip(r-4.4)stochvolTMB_0.2.0.zip(r-4.3)
stochvolTMB_0.2.0.tgz(r-4.4-x86_64)stochvolTMB_0.2.0.tgz(r-4.4-arm64)stochvolTMB_0.2.0.tgz(r-4.3-x86_64)stochvolTMB_0.2.0.tgz(r-4.3-arm64)
stochvolTMB_0.2.0.tar.gz(r-4.5-noble)stochvolTMB_0.2.0.tar.gz(r-4.4-noble)
stochvolTMB.pdf |stochvolTMB.html✨
stochvolTMB/json (API)
NEWS

# 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:

5.02 score 8 stars 26 scripts 232 downloads 8 exports 39 dependencies

Last updated 3 years agofrom:6d42641df7. Checks:OK: 9. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Nov 04 2024
R-4.5-win-x86_64	OK	Nov 04 2024
R-4.5-linux-x86_64	OK	Nov 04 2024
R-4.4-win-x86_64	OK	Nov 04 2024
R-4.4-mac-x86_64	OK	Nov 04 2024
R-4.4-mac-aarch64	OK	Nov 04 2024
R-4.3-win-x86_64	OK	Nov 04 2024
R-4.3-mac-x86_64	OK	Nov 04 2024
R-4.3-mac-aarch64	OK	Nov 04 2024

Exports:demo estimate_parameters get_nll logit plot_forecast residuals sim_sv simulate_parameters

Dependencies:cli colorspace data.table fansi farver ggplot2 glue gtable isoband labeling lattice lifecycle magrittr MASS Matrix MatrixModels mgcv mnormt munsell nlme numDeriv pillar pkgconfig quantreg R6 RColorBrewer Rcpp RcppEigen rlang scales sn SparseM survival tibble TMB utf8 vctrs viridisLite withr

stochvolTMB: Likelihood estimation of stochastic volatility

Jens Christian Wahl

Rendered fromstochvolTMB_vignette.Rmdusingknitr::rmarkdownon Nov 04 2024.

Last update: 2021-08-09
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