NAG Library Function Document

nag_rand_agarchI (g05pdc)

void	nag_rand_agarchI (Nag_ErrorDistn dist, Integer num, Integer ip, Integer iq, const double theta[], double gamma, Integer df, double ht[], double et[], Nag_Boolean fcall, double r[], Integer lr, Integer state[], NagError *fail)

3 Description

A type I

AGARCH (p, q)

process can be represented by:

h_{t} = α_{0} + \sum_{i = 1}^{q} α_{i} {(ε_{t - i} + γ)}^{2} + \sum_{i = 1}^{p} β_{i} h_{t - i}, t = 1, 2, \dots, T;

where

ε_{t} ∣ ψ_{t - 1} = N (0, h_{t})

ε_{t} ∣ ψ_{t - 1} = S_{t} (df, h_{t})

. Here

S_{t}

is a standardized Student's

t

-distribution with

df

degrees of freedom and variance

h_{t}

T

is the number of observations in the sequence,

ε_{t}

is the observed value of the

GARCH (p, q)

process at time

t

h_{t}

is the conditional variance at time

t

, and

ψ_{t}

the set of all information up to time

t

. Symmetric GARCH sequences are generated when

γ

is zero, otherwise asymmetric GARCH sequences are generated with

γ

specifying the amount by which positive (or 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_agarchI (g05pdc).

4 References

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

5 Arguments

1: dist – Nag_ErrorDistnInput

On entry: the type of distribution to use for

ε_{t}

$dist = Nag_NormalDistn$: A Normal distribution is used.
$dist = Nag_Tdistn$: A Student's $t$ -distribution is used.

Constraint:

dist = Nag_NormalDistn

Nag_Tdistn

2: num – IntegerInput

On entry:

T

, the number of terms in the sequence.

Constraint:

num \geq 0

3: ip – IntegerInput

On entry: the number of coefficients,

β_{i}

, for

i = 1, 2, \dots, p

Constraint:

ip \geq 0

4: iq – IntegerInput

On entry: the number of coefficients,

α_{i}

, for

i = 1, 2, \dots, q

Constraint:

iq \geq 1

5: theta[ $iq + ip + 1$ ] – const doubleInput

On entry: the first element must contain the coefficient

α_{o}

, the next iq elements must contain the coefficients

α_{i}

, for

i = 1, 2, \dots, q

. The remaining ip elements must contain the coefficients

β_{j}

, for

j = 1, 2, \dots, p

Constraints:

$\sum_{i = 2}^{iq + ip + 1} theta [i - 1] < 1.0$ ;
$theta [i - 1] \geq 0.0$ , for $i = 2, 3, \dots, ip + iq + 1$ .

6: gamma – doubleInput

On entry: the asymmetry parameter

γ

for the

GARCH (p, q)

sequence.

7: df – IntegerInput

On entry: the number of degrees of freedom for the Student's

t

-distribution.

dist = Nag_NormalDistn

, df is not referenced.

Constraint: if

dist = Nag_Tdistn

df > 2

8: ht[num] – doubleOutput

On exit: the conditional variances

h_{t}

, for

t = 1, 2, \dots, T

, for the

GARCH (p, q)

sequence.

9: et[num] – doubleOutput

On exit: the observations

ε_{t}

, for

t = 1, 2, \dots, T

, for the

GARCH (p, q)

sequence.

10: fcall – Nag_BooleanInput

On entry: if

fcall = Nag_TRUE

, 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

fcall = Nag_FALSE

On exit: contains information that can be used in a subsequent call of nag_rand_agarchI (g05pdc), with

fcall = Nag_FALSE

12: lr – IntegerInput

On entry: the dimension of the array r.

Constraint:

lr \geq 2 \times (ip + iq + 2)

13: state[ $\dim$ ] – IntegerCommunication Array

Note: the dimension,

\dim

, of this array is dictated by the requirements of associated functions that must have been previously called. This array MUST be the same array passed as argument state in the previous call to 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).

6 Error Indicators and Warnings

NE_BAD_PARAM: On entry, argument $⟨value⟩$ had an illegal value.
NE_INT: On entry, $df = ⟨value⟩$ .
Constraint: $df \geq 3$ .
On entry, $ip = ⟨value⟩$ .
Constraint: $ip \geq 0$ .
On entry, $iq = ⟨value⟩$ .
Constraint: $iq \geq 1$ .
On entry, lr is not large enough, $lr = ⟨value⟩$ : minimum length required $= ⟨value⟩$ .
On entry, $num = ⟨value⟩$ .
Constraint: $num \geq 0$ .
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 $ip = ⟨value⟩$ and $ip = ⟨value⟩$ .
Previous value of $iq = ⟨value⟩$ and $iq = ⟨value⟩$ .
NE_REAL_ARRAY: On entry, sum of $theta [i - 1]$ , for $i = 2, 3, \dots, ip + iq + 1$ is $\geq 1.0$ : $sum = ⟨value⟩$ .
On entry, $theta [⟨value⟩] = ⟨value⟩$ .
Constraint: $theta [i - 1] \geq 0.0$ .

7 Accuracy

Not applicable.

8 Parallelism and Performance

nag_rand_agarchI (g05pdc) is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.

Please consult the Users' Note for your implementation for any additional implementation-specific information.

9 Further Comments

None.

10 Example

This example first calls nag_rand_init_repeatable (g05kfc) to initialize a base generator then calls nag_rand_agarchI (g05pdc) to generate two realizations, each consisting of ten observations, from a symmetric