loss_functions

Currently without use. Use the methods derived from
this generic.

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.

Dominik Schulz

fEGarch

SM/LM EGARCH & GARCH, VaR/ES Backtesting & Dual LM Extensions

Yuanhua Feng

Christian Peitz

Oliver Kojo Ayensu

Thomas Gries

Sikandar Siddiqui

Shujie Li

loss_functions function

<dl><dt>object</dt>
<dd>currently without use.</dd>
<dt>penalty</dt>
<dd>currently without use.</dd>
<dt>...</dt>
<dd>currently without use.</dd></dl>

Arguments

Generic for Loss Function Calculation — loss_functions

<dl>

<dt>object</dt>
<dd>currently without use.</dd>


<dt>penalty</dt>
<dd>currently without use.</dd>


<dt>...</dt>
<dd>currently without use.</dd>

</dl>

loss_functions: Generic for Loss Function Calculation

Description

Usage

Value

Arguments