Rmetrics - Autoregressive Conditional Heteroskedastic Modelling
Modelling heterskedasticity in financial time series
Class "fGARCH" - fitted ARMA-GARCH/APARCH models
Class "fGARCHSPEC"
Class 'fUGARCHSPEC'
Absolute moments of GARCH distributions
Standardized generalized error distribution
Generalized error distribution parameter estimation
Skew generalized error distribution
Skew generalized error distribution parameter estimation
Visualise skew normal, (skew) Student-t and (skew) GED distributions
Skew normal distribution
Skew normal distribution parameter estimation
Skew Student-t distribution
Skew Student-t distribution parameter estimation
Standardized Student-t distribution
Student-t distribution parameter estimation
Time series datasets
Fit univariate and multivariate GARCH-type models
Control GARCH fitting algorithms
Simulate univariate GARCH/APARCH time series
Univariate GARCH/APARCH time series specification
GARCH coefficients methods
Extract GARCH model fitted values
Extract GARCH model formula
GARCH plot methods
GARCH prediction function
Extract GARCH model residuals
fGARCH method for the summary function
Extract GARCH model volatility
Diagnostic plots and statistics for fitted GARCH models
Compute Value-at-Risk (VaR) and expected shortfall (ES)
Analyze and model heteroskedastic behavior in financial time series.
Useful links