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: 2026-02-10 07:10:56 UTC; Dominik Schulz
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)
Repository: CRAN
Date/Publication: 2026-02-10 09:40:02 UTC
Built: R 4.6.0; x86_64-w64-mingw32; 2026-05-30 03:31:25 UTC; windows
Archs: x64
