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NAG[g05hlc] NAG[nag_generate_agarchII] - Univariate time series, generate  terms of a GARCH process with asymmetry of the form ![(|epsilon[t-1]|,+,gamma,epsilon[t-1])^2](/support/helpjp/helpview.aspx?si=9870/file08487/math8.png)
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Calling Sequence
g05hlc(p, q, theta, gamma_value, ht, et, fcall, rvec, 'num'=num, 'fail'=fail)
nag_generate_agarchII(. . .)
Parameters
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p - integer;
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On entry: the GARCH argument  .
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Constraint:  . .
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q - integer;
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On entry: the GARCH argument  .
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Constraint:  . .
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theta - Vector(1.. , datatype=float[8]);
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gamma_value - float;
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On entry: the asymmetry argument  for the GARCH sequence.
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ht - Vector(1..num, datatype=float[8]);
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et - Vector(1..num, datatype=float[8]);
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fcall - String;
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On entry: if  , a new sequence is to be generated, else if  a given sequence is to be continued using the information in rvec.
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rvec - Vector(1.. , datatype=float[8]);
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Note: the dimension, dim, of the array rvec must be at least  .
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On entry: the array contains information required to continue a sequence if  .
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On exit: contains information that can be used in a subsequent call of nag_generate_agarchII (g05hlc), with  .
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'num'=num - integer; (optional)
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Default value: the first dimension of the arrays ht, et.
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On entry:  , the number of terms in the sequence.
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 ;
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 .
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'fail'=fail - table; (optional)
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The NAG error argument, see the documentation for NagError.
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Description
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Purpose
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nag_generate_agarchII (g05hlc) generates a given number of terms of a type II AGARCH process (see Engle and Ng (1993)).
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Error Indicators and Warnings
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"NE_2_INT_ARG_LT"
On entry,  while  . These arguments must satisfy  .
"NE_BAD_PARAM"
On entry, argument fcall had an illegal value.
"NE_INT_ARG_LT"
On entry, p must not be less than 0:  .
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Examples
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num := 300:
p := 1:
q := 1:
gamma_value := -0.4:
fcall := "Nag_Garch_Fcall_True":
theta := Vector([0.2, 0.2, 0.7], datatype=float[8]):
ht := Vector(300, datatype=float[8]):
et := Vector(300, datatype=float[8]):
rvec := Vector([0, 0, 0, 0, 0, 0], datatype=float[8]):
NAG:-g05hlc(p, q, theta, gamma_value, ht, et, fcall, rvec, 'num' = num):
# This routine returns a random sequence
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See Also
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Bollerslev T (1986) Generalised autoregressive conditional heteroskedasticity Journal of Econometrics 31 307–327
Engle R (1982) Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation Econometrica 50 987–1008
Engle R and Ng V (1993) Measuring and Testing the Impact of News on Volatility Journal of Finance 48 1749–1777
Hamilton J (1994) Time Series Analysis Princeton University Press
g05 Chapter Introduction.
NAG Toolbox Overview.
NAG Web Site.
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