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
- omegatensor_like of
float omega > 0, mean variance
- alpha_1tensor_like of
float alpha_1 >= 0, autoregressive term coefficient
- beta_1tensor_like of
float beta_1 >= 0, alpha_1 + beta_1 < 1, moving average term coefficient
- initial_voltensor_like of
float initial_vol >= 0, initial volatility, sigma_0
- omegatensor_like of
Methods
GARCH11.dist(omega, alpha_1, beta_1, ...[, ...])Creates a tensor variable corresponding to the cls distribution.