pymc.GARCH11#

class pymc.GARCH11(*args, steps=None, **kwargs)[source]#

GARCH(1,1) with Normal innovations. The model is specified by

\[y_t \sim N(0, \sigma_t^2)\]

\[\sigma_t^2 = \omega + \alpha_1 * y_{t-1}^2 + \beta_1 * \sigma_{t-1}^2\]

where sigma_t^2 (the error variance) follows a ARMA(1, 1) model.

Parameters

omega: tensor: omega > 0, mean variance
alpha_1: tensor: alpha_1 >= 0, autoregressive term coefficient
beta_1: tensor: beta_1 >= 0, alpha_1 + beta_1 < 1, moving average term coefficient
initial_vol: tensor: initial_vol >= 0, initial volatility, sigma_0

Methods

`GARCH11.__init__`(args, *kwargs)
`GARCH11.dist`(omega, alpha_1, beta_1, ...[, ...])	Creates a tensor variable corresponding to the cls distribution.
`GARCH11.rv_op`(omega, alpha_1, beta_1, ...[, ...])