Package: fEGarch
Type: Package
Title: SM/LM EGARCH & GARCH, VaR/ES Backtesting & Dual LM Extensions
Version: 1.0.6
Authors@R: 
    c(person(given = "Dominik",
        family = "Schulz",
        role = c("aut", "cre"),
        email = "dominik.schulz@uni-paderborn.de",
        comment = "Paderborn University, Germany"),
      person(given = "Yuanhua",
        family = "Feng",
        role = "aut",
        email = "yuanhua.feng@uni-paderborn.de",        
        comment = "Paderborn University, Germany"),
      person(given = "Christian",
        family = "Peitz",
        role = c("aut"),
        comment = "Financial Intelligence Unit (German Government)"),
      person(given = "Oliver Kojo",
        family = "Ayensu",
        role = c("aut"),
        email = "oliver.kojo.ayensu@uni-paderborn.de",
        comment = "Paderborn University, Germany"),
      person(given = "Thomas", family = "Gries",
        role = "ctb",
        comment = "Paderborn University, Germany"),
      person(given = "Sikandar", family = "Siddiqui",
        role = "ctb",
        comment = "Deloitte Audit Analytics GmbH, Frankfurt, Germany"),
      person(given = "Shujie",
        family = "Li",
        role = "ctb",
        comment = "Paderborn University, Germany"))             
Maintainer: Dominik Schulz <dominik.schulz@uni-paderborn.de>
Description: Implement and fit a variety of short-memory (SM) and
        long-memory (LM) models from a very broad family of exponential
        generalized autoregressive conditional heteroskedasticity
        (EGARCH) models, such as a MEGARCH (modified EGARCH), FIEGARCH
        (fractionally integrated EGARCH), FIMLog-GARCH (fractionally
        integrated modulus Log-GARCH), and more. The FIMLog-GARCH as
        part of the EGARCH family is discussed in Feng et al. (2023)
        <https://econpapers.repec.org/paper/pdnciepap/156.htm>. For
        convenience and the purpose of comparison, a variety of other
        popular SM and LM GARCH-type models, like an APARCH model, a
        fractionally integrated APARCH (FIAPARCH) model, standard GARCH
        and fractionally integrated GARCH (FIGARCH) models, GJR-GARCH
        and FIGJR-GARCH models, TGARCH and FITGARCH models, are
        implemented as well as dual models with simultaneous modelling
        of the mean, including dual long-memory models with a
        fractionally integrated autoregressive moving average (FARIMA)
        model in the mean and a long-memory model in the variance, and
        semiparametric volatility model extensions. Parametric models
        and parametric model parts are fitted through
        quasi-maximum-likelihood estimation. Furthermore, common
        forecasting and backtesting functions for value-at-risk (VaR)
        and expected shortfall (ES) based on the package's models are
        provided.
License: GPL-3
LinkingTo: Rcpp, RcppArmadillo
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.3.2
Collate: 'AttachMessage.R' 'RcppExports.R' 'close_to_lreturn.R'
        'lin_filters.R' 'hessCalc.R' 'arma-farima-wrappers.R'
        'format_applier_ts.R' 'ts_split_train_and_test.R'
        'snorm-distribution-functions.R'
        'sstd-distribution-functions.R' 'sged-distribution-functions.R'
        'sald-distribution-functions.R' 'base_sim_functions.R'
        'density_selectors.R' 'generics.R'
        'input_checkers_egarch_spec.R' 'input_checkers_mean_spec.R'
        'input_checkers_nonpar_spec.R' 'class-mean_spec.R'
        'class-locpol_spec.R' 'class-fEGarch_fit.R'
        'class-egarch-spec.R' 'fitting-function.R' 'sim-functions.R'
        'nonparametric-step.R' 'setup-estim.R'
        'general_garch_fitting.R' 'aparchfit.R' 'gjrgarchfit.R'
        'tgarchfit.R' 'garchfit.R' 'fiaparchfit.R' 'figjrgarchfit.R'
        'fitgarchfit.R' 'figarchfit.R' 'garch_estim.R' 'varescalc.R'
        'datasets.R' 'fEGarch-package.R' 'rugarch-wrappers.R'
        'class-fEGarch_forecast.R' 'forecasting-functions.R'
        'fEGarch_fit-plot.R' 'class-fEGarch_risk.R'
        'ufRisk-functions.R' 'reexport-pipe.R' 'test-functions.R'
        'class-fEGarch_distr_est.R' 'distr_est.R' 'popular-methods.R'
Depends: R (>= 3.5), methods
Imports: Rcpp (>= 1.0.9), Rsolnp, smoots, esemifar, zoo, stats, utils,
        rugarch, future, furrr, rlang, ggplot2, magrittr, cli, numDeriv
Suggests: testthat (>= 3.0.0)
Config/testthat/edition: 3
NeedsCompilation: yes
Packaged: 2026-06-10 07:41:17 UTC; root
Author: Dominik Schulz [aut, cre] (Paderborn University, Germany),
        Yuanhua Feng [aut] (Paderborn University, Germany), Christian
        Peitz [aut] (Financial Intelligence Unit (German Government)),
        Oliver Kojo Ayensu [aut] (Paderborn University, Germany),
        Thomas Gries [ctb] (Paderborn University, Germany), Sikandar
        Siddiqui [ctb] (Deloitte Audit Analytics GmbH, Frankfurt,
        Germany), Shujie Li [ctb] (Paderborn University, Germany)
Config/pak/sysreqs: cmake
Repository: https://dschulz13.r-universe.dev
Date/Publication: 2026-02-10 09:40:02 UTC
RemoteUrl: https://github.com/cran/fEGarch
RemoteRef: HEAD
RemoteSha: 80deeb1d7034f90e0a3d83929e672a2918e0390b