nag_rand_garchGJR (g05pfc) 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 functions
nag_rand_init_repeatable (g05kfc) (for a repeatable sequence if computed sequentially) or
nag_rand_init_nonrepeatable (g05kgc) (for a non-repeatable sequence) must be called prior to the first call to nag_rand_garchGJR (g05pfc).
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:
dist – Nag_ErrorDistnInput
On entry: the type of distribution to use for
.
- A Normal distribution is used.
- A Student's -distribution is used.
Constraint:
or .
- 2:
num – IntegerInput
-
On entry:
, the number of terms in the sequence.
Constraint:
.
- 3:
ip – IntegerInput
-
On entry: the number of coefficients, , for .
Constraint:
.
- 4:
iq – IntegerInput
-
On entry: the number of coefficients, , for .
Constraint:
.
- 5:
theta[] – const doubleInput
-
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:
gamma – doubleInput
-
On entry: the asymmetry parameter for the sequence.
Constraint:
, for .
- 7:
df – IntegerInput
On entry: the number of degrees of freedom for the Student's
-distribution.
If
,
df is not referenced.
Constraint:
if , .
- 8:
ht[num] – doubleOutput
-
On exit: the conditional variances , for , for the sequence.
- 9:
et[num] – doubleOutput
-
On exit: the observations , for , for the sequence.
- 10:
fcall – Nag_BooleanInput
-
On entry: if
, a new sequence is to be generated, otherwise a given sequence is to be continued using the information in
r.
- 11:
r[lr] – doubleInput/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 nag_rand_garchGJR (g05pfc), with .
- 12:
lr – IntegerInput
-
On entry: the dimension of the array
r.
Constraint:
.
- 13:
state[] – IntegerCommunication Array
-
Note: the actual argument supplied must be the array
state supplied to the initialization functions
nag_rand_init_repeatable (g05kfc) or
nag_rand_init_nonrepeatable (g05kgc).
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:
fail – NagError *Input/Output
-
The NAG error argument (see
Section 3.6 in the Essential Introduction).
- NE_BAD_PARAM
On entry, argument had an illegal value.
- NE_INT
On entry, .
Constraint: .
On entry, .
Constraint: .
On entry, .
Constraint: .
On entry,
lr is not large enough,
: minimum length required
.
On entry, .
Constraint: .
- NE_INTERNAL_ERROR
An internal error has occurred in this function. Check the function call and any array sizes. If the call is correct then please contact
NAG for assistance.
- NE_INVALID_STATE
On entry,
state vector has been corrupted or not initialized.
- NE_PREV_CALL
ip or
iq is not the same as when
r was set up in a previous call.
Previous value of
and
.
Previous value of
and
.
- NE_REAL_2
On entry, and .
Constraint: .
- NE_REAL_ARRAY
On entry, sum of .
Constraint: sum of , for is .
On entry, .
Constraint: .
Not applicable.
None.
This example first calls
nag_rand_init_repeatable (g05kfc) to initialize a base generator then calls nag_rand_garchGJR (g05pfc) to generate two realizations, each consisting of ten observations, from a GJR
model.
None.