pymc.GARCH11#

class pymc.GARCH11(*args, **kwargs)[source]#

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

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

with z_t iid and Normal with mean zero and unit standard deviation.

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__(omega, alpha_1, beta_1, ...)

GARCH11.dist(*args, **kwargs)

Creates a tensor variable corresponding to the cls distribution.

GARCH11.get_volatility(x)

GARCH11.logp(x)

Calculate log-probability of GARCH(1, 1) distribution at specified value.

Attributes

rv_class

rv_op