g13fbc forecasts the conditional variances,
,
from a type I AGARCH
sequence, where
is the forecast horizon (see
Engle and Ng (1993)).
Assume the standard
GARCH
process can be represented by:
or type I AGARCH
process with conditional variance
given by:
has been modelled by
g13fac and the estimated conditional variances and residuals are contained in the arrays
ht and
et respectively. Then
g13fbc will use the last
elements of the arrays
ht and
et to estimate the conditional variance forecasts,
, where
and
is the forecast horizon.
Engle R (1982) Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation Econometrica 50 987–1008
-
1:
– Integer
Input
-
On entry: the number of terms in the arrays
ht and
et from the modelled sequence.
Constraint:
.
-
2:
– Integer
Input
-
On entry: , the forecast horizon.
Constraint:
.
-
3:
– Integer
Input
-
On entry: the GARCH argument .
Constraint:
.
-
4:
– Integer
Input
-
On entry: the GARCH argument .
Constraint:
.
-
5:
– const double
Input
-
On entry: the first element must contain the coefficient
and the next
q elements must contain the coefficients
, for
. The remaining
p elements must contain the coefficients
, for
.
-
6:
– double
Input
-
On entry: the asymmetry argument for the GARCH sequence.
-
7:
– double
Output
-
On exit: the forecast values of the conditional variance, , for .
-
8:
– const double
Input
-
On entry: the sequence of past conditional variances for the GARCH process, , for .
-
9:
– const double
Input
-
On entry: the sequence of past residuals for the GARCH process, , for .
-
10:
– NagError *
Input/Output
-
The NAG error argument (see
Section 7 in the Introduction to the NAG Library CL Interface).
Not applicable.
Background information to multithreading can be found in the
Multithreading documentation.
None.