Package dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch

Class Summary

Class	Description
GARCH11Model	An GARCH11 model takes this form. \[ h_t = \alpha_0 + \alpha_1 e_{t-1}^2 + \beta_1 h_{t-1} \]
GARCHFit	This implementation fits, for a data set, a Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model by maximizing the likelihood function using the gradient information.
GARCHModel	The GARCH(p, q) model takes this form. \[ h_t = \alpha_0 + \sum_{i=1}^{q} (\alpha_i e_{t-i}^2) + \sum_{i=1}^{p} (\beta_i h_{t-i}) \] p is the order of the GARCH terms \(h_{t-i}\); q is the order of the ARCH terms \(e_{t-i}^{2}\).
GARCHSim	This class simulates the GARCH models of this form. \[ h_t = \alpha_0 + \sum_{i=1}^{q} (\alpha_i e_{t-i}^2) + \sum_{i=1}^{p} (\beta_i h_{t-i}) \] p is the order of the GARCH terms \(h_{t-i}\); q is the order of the ARCH terms \(e_{t-i}^{2}\).

Enum Summary

Enum	Description
GARCHFit.GRADIENT	the available methods to compute the gradient to guild the optimization search