Wrapping bgev as an innovation distribution | Fit, simulate, forecast | Reference
Package: GEVStableGarch 2.0.0
Thiago do Rego Sousa
GEVStableGarch: ARMA-GARCH/APARCH Models with Stable, GEV and GAt Innovations
Simulation, estimation and forecasting of autoregressive moving average (ARMA) models with generalized autoregressive conditional heteroskedasticity (GARCH) or asymmetric power ARCH (APARCH, with ARCH the autoregressive conditional heteroskedasticity model) errors, with stable (S0 parametrization), generalized extreme value (GEV) and generalized asymmetric t (GAt) innovations. The innovation distribution is a pluggable object, so user defined densities get the same fitting, simulation, stationarity and forecasting machinery.
Authors:
GEVStableGarch_2.0.0.tar.gz
GEVStableGarch_2.0.0.zip(r-4.7-any)GEVStableGarch_2.0.0.zip(r-4.6-any)
GEVStableGarch_2.0.0.tar.gz(r-4.7-any)GEVStableGarch_2.0.0.tar.gz(r-4.6-any)
GEVStableGarch_2.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
GEVStableGarch/json (API)
| # Install 'GEVStableGarch' in R: |
| install.packages('GEVStableGarch', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/thiagodoregosousa/gevstablegarch/issues
Last updated from:d7c302ab22. Checks:6 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 181 | ||
| source / vignettes | OK | 211 | ||
| linux-release | OK | 174 | ||
| windows-devel | OK | 82 | ||
| windows-release | OK | 95 | ||
| wasm-release | OK | 125 |
Exports:dgatgs_aparch_momentgs_bootstrapgs_check_distgs_distgs_fitgs_gatgs_gevgs_modelgs_simgs_specgs_stablegs_stationaritypgatqgatrgat
Dependencies:codetoolsfuturefuture.applyglobalslibstable4ulistenvnumDerivparallellyRcppRcppArmadilloRcppGSLRsolnptruncnorm
A new innovation distribution: bimodal GEV from the bgev package
Last update: 2026-11-03
Started: 2026-11-03
Monte Carlo simulation study
Part A — non-degradation against the 2012 thesis | Consistency | Part B — standard-error calibration (Wald coverage) | Bootstrap vs Wald for stable mean/AR parameters | Reference
Last update: 2026-11-03
Started: 2026-11-03
Stable, GEV and GAt ARMA-APARCH models
The model | GEV AR(1)-APARCH(1,1) | Forecasting and value at risk | A user defined innovation distribution | Relationship to other packages | References
Last update: 2026-11-03
Started: 2026-11-03
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Generalized Asymmetric t (GAT) Distribution | dgat GAT gat pgat qgat rgat |
| APARCH moment of the innovation distribution | gs_aparch_moment |
| Parametric bootstrap standard errors and confidence intervals | gs_bootstrap |
| Self check of an innovation distribution | gs_check_dist |
| Innovation distribution for ARMA-GARCH/APARCH models | gs_dist |
| Fit an ARMA-GARCH/APARCH model by maximum likelihood | gs_fit |
| Methods for fitted models | coef.gs_fit fitted.gs_fit gs_fit-methods logLik.gs_fit nobs.gs_fit print.gs_fit residuals.gs_fit sigma.gs_fit summary.gs_fit vcov.gs_fit |
| GAt innovations | gs_gat |
| GEV innovations | gs_gev |
| Set the parameters of an ARMA-GARCH/APARCH model | gs_model |
| Simulate an ARMA-GARCH/APARCH model | gs_sim |
| Specify an ARMA-GARCH/APARCH model | gs_spec |
| Stable innovations (S0 parametrization) | gs_stable |
| Persistence of the APARCH scale recursion | gs_stationarity |
| Forecast an ARMA-GARCH/APARCH model | predict.gs_fit predict.gs_model |