Returns an array of the fitted (in-sample) conditional mean values.
X is the univariate time series data (a one dimensional array of cells (e.g. rows or columns)).
Order is the time order in the data series (i.e. the first data point's corresponding date (earliest date=1 (default), latest date=0)).
|1||ascending (the first data point corresponds to the earliest date) (default)|
|0||descending (the first data point corresponds to the latest date)|
mean is the GARCH-M model mean (i.e. mu).
lambda is the volatility coefficient for the mean. In finance, lambda is referenced as the risk premium.
alphas are the parameters of the ARCH(p) component model (starting with the lowest lag).
betas are the parameters of the GARCH(q) component model (starting with the lowest lag).
- The underlying model is described here.
- The time series is homogeneous or equally spaced.
- The time series may include missing values (e.g. #N/A) at either end.
- The fitted conditonal mean is calculated as:
$$\hat x_t = \mu + \lambda \sigma_t$$
- $\hat x_t$ is the fitted conditional mean at time t.
- $\sigma_t$ is the fitted conditional volatility at time t.
- The number of parameters in the input argument - alpha - determines the order of the ARCH component model.
- The number of parameters in the input argument - beta - determines the order of the GARCH component model.