A library of quasi maximum-likelihood estimation (QMLE) methods for fitting various short- and long-memory models from a broad family of exponential generalized autoregressive conditional heteroskedasticity (EGARCH) models. For the purpose of comparison, a FIAPARCH (fractionally integrated asymmetric power ARCH), a FIGARCH (fractionally integrated GARCH), a FITGARCH, a FIGJR-GARCH and their short-memory variants can be implemented as well.
The main functions of the package are:
fEGarch_spec:setting the model specifications for a model from the broader EGARCH family,
mean_spec:setting the model specifications for the conditional mean,
fEGarch:fitting a model from the broad family of EGARCH models given a model specification and an observation series,
garchm_estim:fitting a GARCH-type model selectable from a standard GARCH, a GJR-GARCH, a TGARCH, an APARCH, a FIGARCH, a FIGJR-GARCH, a FITGARCH and a FIAPARCH,
fEGarch_sim:simulating from an EGARCH family model,
fiaparch_sim:simulating from a FIAPARCH model,
figarch_sim:simulating from a FIGARCH model,
figjrgarch_sim:simulating from a FIGJR-GARCH model,
fitgarch_sim:simulating from a FITGARCH model,
aparch_sim:simulating from an APARCH model,
garch_sim:simulating from a GARCH model,
gjrgarch_sim:simulating from a GJR-GARCH model,
tgarch_sim:simulating from a TGARCH model,
predict,fEGarch_fit-method:multistep point forecasts of the conditional mean and the conditional standard deviation,
predict_roll,fEGarch_fit-method:rolling point forecasts of the conditional mean and the conditional standard deviation over a test set.
measure_risk:value at risk and expected shortfall computation for various model specifications.
find_dist:fits all eight distributions considered in this package to a supposed iid series and selects the best fitted distribution following either BIC (the default) or AIC.
backtest_suite,fEGarch_risk-method:runs a selection of functions for backtesting VaR and ES.
The package includes a few datasets. Follow the corresponding links to the documentation of the datasets to find additional information including the sources.
UKinflation:monthly inflation rate of the UK.
SP500:daily log-returns of the S&P 500 index.
The package is distributed under the General Public License v3 ([GPL-3](https://tldrlegal.com/license/gnu-general-public-license-v3-(gpl-3))).
Dominik Schulz (Department of Economics, Paderborn
University),
Author and Package Creator
Yuanhua Feng (Department of Economics, Paderborn
University),
Author
Christian Peitz (Financial Intelligence Unit, German Government),
Author
Oliver Kojo Ayensu (Department of Economics, Paderborn
University),
Author
fEGarch is an R package for estimating a broad family of
EGARCH models (Feng et al., 2025; Ayensu et al., 2025)
including both short- and long-memory as well as a
selection of varying transformations for the asymmetry and the
magnitude term in such a model, for example in form of the
FIMLog-GARCH (Feng et al., 2023). Log-GARCH specifications can be
implemented as well as a special case of the broad EGARCH family.
The six most common conditional distributions are supported, namely
a normal distribution, a \(t\)- distribution, a generalized error
distribution, as well as the skewed variants of these three
distributions. Furthermore, as a novelty, an average Laplace (AL)
distribution (see for example Feng et al., 2025) and its skewed
version are provided as well.
The main functions to implement these models are
fEGarch_spec in combination with
fEGarch. Further details on these models can also
be found in the documentation of these two functions. For
convenience, further specification functions for particular
submodels are available as well: egarch_spec,
loggarch_spec, megarch_spec,
mloggarch_spec, fiegarch_spec,
filoggarch_spec,
fimegarch_spec and fimloggarch_spec.
As a popular alternative for the sake of comparison, a FIAPARCH
model can be fitted as well using fiaparch. The
corresponding documentation page also includes further information
on the model. Similarly, figarch can be utilized for
fitting FIGARCH models, fitgarch for fitting
FITGARCH models and figjrgarch for fitting
FIGJR-GARCH models. A general function for the estimation of additional
GARCH-type models, including the aforementioned additional models
as well as their short-memory variants, is
garchm_estim.
In addition, the package provides functionalities in order to
simultaneously model the conditional mean (using either autoregressive
moving-average (ARMA) models or fractionally integrated ARMA (FARIMA)
models) alongside the
conditional variance. For this purpose, the function
mean_spec can be utilized and its result needs to be
passed to fEGarch alongside the result of either
fEGarch_spec or one of its wrappers.
Further options include the specification of semiparametric volatility models (see also Ayensu et al., 2025), where a smooth, nonparametric scale function is at first estimated and removed from an observed series, before estimating a parametric model. The scale estimation is currently done through automated local polynomial regression with designated bandwidth selection algorithms under short memory and long memory.
Ayensu, O. K., Feng, Y., & Schulz, D. (2025). Recent Extensions of Exponential GARCH Models: Theory and Application. Forthcoming preprint, Paderborn University.
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