fGarch4052.93 package

Rmetrics - Autoregressive Conditional Heteroskedastic Modelling

00fGarch-package

Modelling heterskedasticity in financial time series

class-fGARCH

Class "fGARCH" - fitted ARMA-GARCH/APARCH models

class-fGARCHSPEC

Class "fGARCHSPEC"

class-fUGARCHSPEC

Class 'fUGARCHSPEC'

dist-absMoments

Absolute moments of GARCH distributions

dist-ged

Standardized generalized error distribution

dist-gedFit

Generalized error distribution parameter estimation

dist-sged

Skew generalized error distribution

dist-sgedFit

Skew generalized error distribution parameter estimation

dist-Slider

Visualise skew normal, (skew) Student-t and (skew) GED distributions

dist-snorm

Skew normal distribution

dist-snormFit

Skew normal distribution parameter estimation

dist-sstd

Skew Student-t distribution

dist-sstdFit

Skew Student-t distribution parameter estimation

dist-std

Standardized Student-t distribution

dist-stdFit

Student-t distribution parameter estimation

fGarchData

Time series datasets

garchFit

Fit univariate and multivariate GARCH-type models

garchFitControl

Control GARCH fitting algorithms

garchSim

Simulate univariate GARCH/APARCH time series

garchSpec

Univariate GARCH/APARCH time series specification

methods-coef

GARCH coefficients methods

methods-fitted

Extract GARCH model fitted values

methods-formula

Extract GARCH model formula

methods-plot

GARCH plot methods

methods-predict

GARCH prediction function

methods-residuals

Extract GARCH model residuals

methods-summary

fGARCH method for the summary function

methods-volatility

Extract GARCH model volatility

stats-tsdiag

Diagnostic plots and statistics for fitted GARCH models

VaR

Compute Value-at-Risk (VaR) and expected shortfall (ES)

Analyze and model heteroskedastic behavior in financial time series.

  • Maintainer: Georgi N. Boshnakov
  • License: GPL (>= 2)
  • Last published: 2025-12-12