<?xml version="1.0" encoding="UTF-8"?>
<oai_dc:dc xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:title>SM/LM EGARCH &amp; GARCH, VaR/ES Backtesting &amp; Dual LM Extensions</dc:title>
  <dc:title>R package fEGarch version 1.0.6</dc:title>
  <dc:subject>CRAN Task View: Finance (https://CRAN.R-project.org/view=Finance)</dc:subject>
  <dc: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)
  &lt;https://econpapers.repec.org/paper/pdnciepap/156.htm&gt;. 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.</dc:description>
  <dc:type>Software</dc:type>
  <dc:relation>Depends: R (&gt;= 3.5), methods</dc:relation>
  <dc:relation>Imports: Rcpp (&gt;= 1.0.9), Rsolnp, smoots, esemifar, zoo, stats, utils,
rugarch, future, furrr, rlang, ggplot2, magrittr, cli, numDeriv</dc:relation>
  <dc:relation>LinkingTo: Rcpp, RcppArmadillo</dc:relation>
  <dc:relation>Suggests: testthat (&gt;= 3.0.0)</dc:relation>
  <dc:creator>Dominik Schulz &lt;dominik.schulz@uni-paderborn.de&gt;</dc:creator>
  <dc:publisher>Comprehensive R Archive Network (CRAN)</dc:publisher>
  <dc:contributor>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)</dc:contributor>
  <dc:rights>GPL-3</dc:rights>
  <dc:date>2026-02-10</dc:date>
  <dc:format>application/tgz</dc:format>
  <dc:identifier>https://CRAN.R-project.org/package=fEGarch</dc:identifier>
  <dc:identifier>doi:10.32614/CRAN.package.fEGarch</dc:identifier>
</oai_dc:dc>