Returns an array of the fitted (in-sample) conditional mean values.
GARCHM_MEAN(X, Order, mean, lambda, alphas, betas)
- is the univariate time series data (a one dimensional array of cells (e.g. rows or columns)).
- is the time order in the data series (i.e. the first data point's corresponding date (earliest date=1 (default), latest date=0)).
Order Description 1 ascending (the first data point corresponds to the earliest date) (default) 0 descending (the first data point corresponds to the latest date)
- is the GARCH-M model mean (i.e. mu).
- is the volatility coefficient for the mean. In finance, lambda is referenced as the risk premium.
- are the parameters of the ARCH(p) component model (starting with the lowest lag).
- 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.