g05pff generates a given number of terms of a GJR
process (see
Glosten et al. (1993)).
A GJR
process is represented by:
where
if
,
if
, and
or
. Here
is a standardized Student's
-distribution with
degrees of freedom and variance
,
is the number of observations in the sequence,
is the observed value of the
process at time
,
is the conditional variance at time
, and
the set of all information up to time
. Symmetric GARCH sequences are generated when
is zero, otherwise asymmetric GARCH sequences are generated with
specifying the amount by which negative shocks are to be enhanced.
One of the initialization routines
g05kff (for a repeatable sequence if computed sequentially) or
g05kgf (for a non-repeatable sequence) must be called prior to the first call to
g05pff.
Engle R (1982) Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation Econometrica 50 987–1008
Glosten L, Jagannathan R and Runkle D (1993) Relationship between the expected value and the volatility of nominal excess return on stocks Journal of Finance 48 1779–1801
- 1: – Character(1)Input
-
On entry: the type of distribution to use for
.
- A Normal distribution is used.
- A Student's -distribution is used.
Constraint:
or .
- 2: – IntegerInput
-
On entry: , the number of terms in the sequence.
Constraint:
.
- 3: – IntegerInput
-
On entry: the number of coefficients,
, for .
Constraint:
.
- 4: – IntegerInput
-
On entry: the number of coefficients,
, for .
Constraint:
.
- 5: – Real (Kind=nag_wp) arrayInput
-
On entry: the first element must contain the coefficient
, the next
iq elements must contain the coefficients
, for
. The remaining
ip elements must contain the coefficients
, for
.
Constraints:
- ;
- , for and .
- 6: – Real (Kind=nag_wp)Input
-
On entry: the asymmetry parameter for the sequence.
Constraint:
, for .
- 7: – IntegerInput
-
On entry: the number of degrees of freedom for the Student's
-distribution.
If
,
df is not referenced.
Constraint:
if , .
- 8: – Real (Kind=nag_wp) arrayOutput
-
On exit: the conditional variances
, for , for the sequence.
- 9: – Real (Kind=nag_wp) arrayOutput
-
On exit: the observations
, for , for the sequence.
- 10: – LogicalInput
-
On entry: if
, a new sequence is to be generated, otherwise a given sequence is to be continued using the information in
r.
- 11: – Real (Kind=nag_wp) arrayInput/Output
-
On entry: the array contains information required to continue a sequence if .
On exit: contains information that can be used in a subsequent call of g05pff, with .
- 12: – IntegerInput
-
On entry: the dimension of the array
r as declared in the (sub)program from which
g05pff is called.
Constraint:
.
- 13: – Integer arrayCommunication Array
-
Note: the actual argument supplied
must be the array
state supplied to the initialization routines
g05kff or
g05kgf.
On entry: contains information on the selected base generator and its current state.
On exit: contains updated information on the state of the generator.
- 14: – IntegerInput/Output
-
On entry:
ifail must be set to
,
. If you are unfamiliar with this argument you should refer to
Section 3.4 in How to Use the NAG Library and its Documentation for details.
For environments where it might be inappropriate to halt program execution when an error is detected, the value
is recommended. If the output of error messages is undesirable, then the value
is recommended. Otherwise, if you are not familiar with this argument, the recommended value is
.
When the value is used it is essential to test the value of ifail on exit.
On exit:
unless the routine detects an error or a warning has been flagged (see
Section 6).
If on entry
or
, explanatory error messages are output on the current error message unit (as defined by
x04aaf).
Not applicable.
Please consult the
X06 Chapter Introduction for information on how to control and interrogate the OpenMP environment used within this routine. Please also consult the
Users' Note for your implementation for any additional implementation-specific information.
None.
This example first calls
g05kff to initialize a base generator then calls
g05pff to generate two realizations, each consisting of ten observations, from a GJR
model.