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

Packages that use dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch

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

Classes in dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch used by dev.nm.stat.timeseries.linear.univariate.stationaryprocess.armagarch

Class and Description
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 \(e_{t-i}^{2}\).

Classes in dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch used by dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch

Class and Description
GARCHFit.GRADIENT the available methods to compute the gradient to guild the optimization search
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 \(e_{t-i}^{2}\).